Ratings.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "2uueph4esSIs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "797ba5c6-f904-4f0f-e380-712d4380a5e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.25.1-py3-none-any.whl (5.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.8/5.8 MB\u001b[0m \u001b[31m69.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (21.3)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m89.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Collecting huggingface-hub<1.0,>=0.10.0\n",
            "  Downloading huggingface_hub-0.11.1-py3-none-any.whl (182 kB)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.11.1 tokenizers-0.13.2 transformers-4.25.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting bert-extractive-summarizer\n",
            "  Downloading bert_extractive_summarizer-0.10.1-py3-none-any.whl (25 kB)\n",
            "Requirement already satisfied: spacy in /usr/local/lib/python3.8/dist-packages (from bert-extractive-summarizer) (3.4.4)\n",
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            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers->bert-extractive-summarizer) (4.4.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.0->spacy->bert-extractive-summarizer) (3.0.9)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests<3.0.0,>=2.13.0->spacy->bert-extractive-summarizer) (2.10)\n",
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            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests<3.0.0,>=2.13.0->spacy->bert-extractive-summarizer) (1.24.3)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests<3.0.0,>=2.13.0->spacy->bert-extractive-summarizer) (4.0.0)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.8/dist-packages (from thinc<8.2.0,>=8.1.0->spacy->bert-extractive-summarizer) (0.0.3)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.8/dist-packages (from thinc<8.2.0,>=8.1.0->spacy->bert-extractive-summarizer) (0.7.9)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.8/dist-packages (from typer<0.8.0,>=0.3.0->spacy->bert-extractive-summarizer) (7.1.2)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.8/dist-packages (from jinja2->spacy->bert-extractive-summarizer) (2.0.1)\n",
            "Installing collected packages: bert-extractive-summarizer\n",
            "Successfully installed bert-extractive-summarizer-0.10.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: spacy in /usr/local/lib/python3.8/dist-packages (3.4.4)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.9.1 in /usr/local/lib/python3.8/dist-packages (from spacy) (0.10.1)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from spacy) (3.0.8)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from spacy) (2.0.7)\n",
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            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.8/dist-packages (from spacy) (2.25.1)\n",
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          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install bert-extractive-summarizer\n",
        "!pip install spacy"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "QE-YM3b4sak5"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "id": "mZBWis1kEC-X",
        "outputId": "cb13f10a-c927-4b12-9002-b3ba9740495e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-49af943f-c677-4979-9343-9ff9eda23f62\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-49af943f-c677-4979-9343-9ff9eda23f62\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Unlabelled test datset.xlsx to Unlabelled test datset.xlsx\n",
            "Saving Dataset Problem statement 3.xlsx to Dataset Problem statement 3.xlsx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import io\n",
        "# df2 = pd.read_csv(io.BytesIO(uploaded['Filename.csv']))\n",
        "dftr = pd.read_excel(io.BytesIO(uploaded['Dataset Problem statement 3.xlsx']))"
      ],
      "metadata": {
        "id": "ul9kQTdvyXoe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "JFGABnpmDUiq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dft = pd.read_excel(io.BytesIO(uploaded['Unlabelled test datset.xlsx']))"
      ],
      "metadata": {
        "id": "exjVMsbH9haO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from summarizer import Summarizer,TransformerSummarizer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvPSSf_XyaPl",
        "outputId": "e4b25f40-9f5c-4757-deb2-0b47b49616d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/torch/cuda/__init__.py:497: UserWarning: Can't initialize NVML\n",
            "  warnings.warn(\"Can't initialize NVML\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "body = '''\n",
        "       Scientists say they have discovered a new species of orangutans on Indonesia’s island of Sumatra.\n",
        "The population differs in several ways from the two existing orangutan species found in Sumatra and the neighboring island of Borneo.\n",
        "The orangutans were found inside North Sumatra’s Batang Toru forest, the science publication Current Biology reported.\n",
        "Researchers named the new species the Tapanuli orangutan. They say the animals are considered a new species because of genetic, skeletal and tooth differences.\n",
        "Michael Kruetzen is a geneticist with the University of Zurich who has studied the orangutans for several years. He said he was excited to be part of the unusual discovery of a new great ape in the present day. He noted that most great apes are currently considered endangered or severely endangered.\n",
        "Gorillas, chimpanzees and bonobos also belong to the great ape species.\n",
        "Orangutan – which means person of the forest in the Indonesian and Malay languages - is the world’s biggest tree-living mammal. The orange-haired animals can move easily among the trees because their arms are longer than their legs. They live more lonely lives than other great apes, spending a lot of time sleeping and eating fruit in the forest.\n",
        "The new study said fewer than 800 of the newly-described orangutans exist. Their low numbers make the group the most endangered of all the great ape species.\n",
        "They live within an area covering about 1,000 square kilometers. The population is considered highly vulnerable. That is because the environment which they depend on is greatly threatened by development.\n",
        "Researchers say if steps are not taken quickly to reduce the current and future threats, the new species could become extinct “within our lifetime.”\n",
        "Research into the new species began in 2013, when an orangutan protection group in Sumatra found an injured orangutan in an area far away from the other species. The adult male orangutan had been beaten by local villagers and died of his injuries. The complete skull was examined by researchers.\n",
        "Among the physical differences of the new species are a notably smaller head and frizzier hair. The Tapanuli orangutans also have a different diet and are found only in higher forest areas.\n",
        "There is no unified international system for recognizing new species. But to be considered, discovery claims at least require publication in a major scientific publication.\n",
        "Russell Mittermeier is head of the primate specialist group at the International Union for the Conservation of Nature. He called the finding a “remarkable discovery.” He said it puts responsibility on the Indonesian government to help the species survive.\n",
        "Matthew Nowak is one of the writers of the study. He told the Associated Press that there are three groups of the Tapanuli orangutans that are separated by non-protected land.He said forest land needs to connect the separated groups.\n",
        "In addition, the writers of the study are recommending that plans for a hydropower center in the area be stopped by the government.\n",
        "It also recommended that remaining forest in the Sumatran area where the orangutans live be protected.\n",
        "I’m Bryan Lynn.\n",
        "\n",
        "        '''"
      ],
      "metadata": {
        "id": "QL0kU8ZMydsD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bert_model = Summarizer()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217,
          "referenced_widgets": [
            "40290b85139c4b14b5030abf09d9d72e",
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        "id": "LVepDlKHyzie",
        "outputId": "58e042df-91b8-4d4f-e5ce-88a9e2308879"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/571 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "40290b85139c4b14b5030abf09d9d72e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.34G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "77cfee47b6694071a178fc3dbd486492"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-large-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a87c757d8f0146c387823b9f1fb7237b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5cc67946547347bf8b09bc13636f2451"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "62z-oEIIAJO4",
        "outputId": "3c4cbc6d-1f80-4d8c-d80d-2a15cf3166cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Product                               Job Role  \\\n",
              "0          IX                                  XYZ   \n",
              "1          IX                            Architect   \n",
              "2          IX                                  PQR   \n",
              "3          IX                     Sales Specialist   \n",
              "4          IX                            Architect   \n",
              "..        ...                                  ...   \n",
              "382        QK  TECHNICAL LEADER.CUSTOMER Executive   \n",
              "383        QK          Engineer - Customer Support   \n",
              "384        QK  TECHNICAL LEADER.CUSTOMER Executive   \n",
              "385        QK           Support.CUSTOMER Executive   \n",
              "386        QK  TECHNICAL LEADER.CUSTOMER Executive   \n",
              "\n",
              "                                    Verbatim Feedback   \\\n",
              "0                        There is no  maintenance menu   \n",
              "1                                          Very buggy.   \n",
              "2                            High quality -  standards   \n",
              "3                                     Features missing   \n",
              "4                          how to do the inter-working   \n",
              "..                                                 ...   \n",
              "382                                  uninteresting GUI   \n",
              "383                                  UI is interactive   \n",
              "384  Totally instable.. Enginieering quallity has d...   \n",
              "385                           Lack of work around this   \n",
              "386  The platform is been wide deploy This trigger ...   \n",
              "\n",
              "     Sentiment (1=Positive & 0= Negative)  \n",
              "0                                       0  \n",
              "1                                       0  \n",
              "2                                       1  \n",
              "3                                       0  \n",
              "4                                       0  \n",
              "..                                    ...  \n",
              "382                                     0  \n",
              "383                                     1  \n",
              "384                                     0  \n",
              "385                                     0  \n",
              "386                                     0  \n",
              "\n",
              "[387 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-87fcb11d-f64a-46d1-96d6-a18244097b12\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Product</th>\n",
              "      <th>Job Role</th>\n",
              "      <th>Verbatim Feedback</th>\n",
              "      <th>Sentiment (1=Positive &amp; 0= Negative)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>IX</td>\n",
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              "      <td>There is no  maintenance menu</td>\n",
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              "      <th>1</th>\n",
              "      <td>IX</td>\n",
              "      <td>Architect</td>\n",
              "      <td>Very buggy.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>IX</td>\n",
              "      <td>PQR</td>\n",
              "      <td>High quality -  standards</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>IX</td>\n",
              "      <td>Sales Specialist</td>\n",
              "      <td>Features missing</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>IX</td>\n",
              "      <td>Architect</td>\n",
              "      <td>how to do the inter-working</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>382</th>\n",
              "      <td>QK</td>\n",
              "      <td>TECHNICAL LEADER.CUSTOMER Executive</td>\n",
              "      <td>uninteresting GUI</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>383</th>\n",
              "      <td>QK</td>\n",
              "      <td>Engineer - Customer Support</td>\n",
              "      <td>UI is interactive</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>384</th>\n",
              "      <td>QK</td>\n",
              "      <td>TECHNICAL LEADER.CUSTOMER Executive</td>\n",
              "      <td>Totally instable.. Enginieering quallity has d...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>385</th>\n",
              "      <td>QK</td>\n",
              "      <td>Support.CUSTOMER Executive</td>\n",
              "      <td>Lack of work around this</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>386</th>\n",
              "      <td>QK</td>\n",
              "      <td>TECHNICAL LEADER.CUSTOMER Executive</td>\n",
              "      <td>The platform is been wide deploy This trigger ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>387 rows × 4 columns</p>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "dftr[dftr['Job Role'] == 'Sales Specialist']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "UQzx77Ct2pA6",
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      "execution_count": null,
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            "text/plain": [
              "    Product            Job Role  \\\n",
              "3          IX  Sales Specialist   \n",
              "6          IX  Sales Specialist   \n",
              "23         QK  Sales Specialist   \n",
              "46         QK  Sales Specialist   \n",
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              "288        QK  Sales Specialist   \n",
              "\n",
              "                                    Verbatim Feedback   \\\n",
              "3                                     Features missing   \n",
              "6                                    Not much deployed   \n",
              "23    Waiting for more \\nTime and Security feature set   \n",
              "46                                                Good   \n",
              "86                                        usage varied   \n",
              "120                                               Good   \n",
              "130                                 Excellent delivery   \n",
              "182                                   missing features   \n",
              "184  Slotting rules are very cumbersome.Too many ru...   \n",
              "252  Product in good competition with lots of varia...   \n",
              "261  Need more optics qualification in synergy with...   \n",
              "288                         Hit the roadmap timelines.   \n",
              "\n",
              "     Sentiment (1=Positive & 0= Negative)  \n",
              "3                                       0  \n",
              "6                                       0  \n",
              "23                                      1  \n",
              "46                                      1  \n",
              "86                                      1  \n",
              "120                                     1  \n",
              "130                                     1  \n",
              "182                                     1  \n",
              "184                                     0  \n",
              "252                                     1  \n",
              "261                                     0  \n",
              "288                                     0  "
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "dftr[['Product  ', 'Job Role']].groupby(['Product  ']).apply(lambda x:' '.join(x['Job Role'].unique()))"
      ],
      "metadata": {
        "colab": {
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        "outputId": "cf9e9a8c-f614-4841-c962-a64e9a75c311"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Product  \n",
              "DX    XYZ Architect Sales Specialist PQR Systems Eng...\n",
              "IX    XYZ Architect PQR Sales Specialist Support Ope...\n",
              "QK    XYZ PQR Architect Leader, Client Executive Sal...\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfk = pd.DataFrame(dft.groupby(['Job Role'], group_keys= False)['Verbatim Feedback '].apply(lambda x:'. '.join(x)))"
      ],
      "metadata": {
        "id": "wMNud2jr0rRU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dfk = dfk.reset_index()"
      ],
      "metadata": {
        "id": "an17EqHG08_h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dfk.columns = ['job', 'feedback']"
      ],
      "metadata": {
        "id": "zqzj4CFR1t5P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "body = dfk['feedback'][2]\n",
        "''.join(bert_model(body, max_length=20, min_length=10))"
      ],
      "metadata": {
        "id": "DhJCBiU92XaX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "bfde6092-5dbf-4af8-9ed0-ea3a8bc71a1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "''"
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              "type": "string"
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          "metadata": {},
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        }
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    {
      "cell_type": "code",
      "source": [
        "dftr['Job Role'].value_counts()"
      ],
      "metadata": {
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        },
        "outputId": "1bd4afb5-9b58-47ca-b4a3-5cea9fb0acca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Architect                                           60\n",
              "Support                                             54\n",
              "XYZ                                                 28\n",
              "Engineer                                            22\n",
              "Architect.SALES                                     19\n",
              "PQR                                                 18\n",
              "Manager                                             16\n",
              "Support.CUSTOMER Executive                          14\n",
              "Sales Specialist                                    12\n",
              "Operations                                          12\n",
              "Systems Engineer                                    11\n",
              "TECHNICAL LEADER.CUSTOMER Executive                 10\n",
              "Leader, Customer Executive                          10\n",
              "Solutions                                            9\n",
              "Engineer.CUSTOMER Executive                          8\n",
              "SYSTEMS ENGINEER.SALES                               7\n",
              "Leader, Client Executive                             6\n",
              "Engineer - Network [C]                               4\n",
              "XYZ.SALES                                            4\n",
              "Technical Leader                                     4\n",
              "SALES SPECIALIST.SALES                               4\n",
              "MANAGER SYSTEMS ENGINEER.SALES                       4\n",
              "ARCHITECT.CUSTOMER Executive                         3\n",
              "Project Manager [C]                                  3\n",
              "CXO                                                  3\n",
              "High Touch Operations Manager                        3\n",
              "Director, Regionalabc                                3\n",
              "MANAGER-REGIONAL.SALES                               2\n",
              "PrincipalabcArchitect                                2\n",
              "Technical Project Manager [C]                        2\n",
              "Sales Business Development Manager                   2\n",
              "XYZ SOLUTIONS.SPabc                                  2\n",
              "HIGH TOUCH OPERATIONS MANAGER.CUSTOMER Executive     2\n",
              "Engineer - Consultant [C]                            2\n",
              "Leader, Regionalabc                                  2\n",
              "CLIENT EXECUTIVE.SALES                               1\n",
              "Leader,abc Specialist                                1\n",
              "VIRTUALabc XYZ.SALES                                 1\n",
              "Engineer - Test/QA [C]                               1\n",
              "MANAGER.CUSTOMER Executive                           1\n",
              "DIRECTOR.CUSTOMER Executive                          1\n",
              "Account Representative [C]                           1\n",
              "Systems Admin [C]                                    1\n",
              "Principal Engineer                                   1\n",
              "Technical Solutions Specialist                       1\n",
              "CUSTOMER PROJECT MANAGER.CUSTOMER Executive          1\n",
              "Engineer - Consultant                                1\n",
              "Business SolutionsArchitect                          1\n",
              "Engineer [C]                                         1\n",
              "Business Development Manager                         1\n",
              "Recruiting                                           1\n",
              "Customer Project Manager                             1\n",
              "Services Manager                                     1\n",
              "Engineer - Customer Support [C]                      1\n",
              "Engineer - Customer Support                          1\n",
              "Name: Job Role, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dft['Job Role'].value_counts()"
      ],
      "metadata": {
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              "    Product                               Job Role  \\\n",
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              "    Product                               Job Role  \\\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U sentence-transformers"
      ],
      "metadata": {
        "id": "HKnG7groEgVK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33ddebd4-cc06-44e4-ca83-7586d939f374"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
            "\u001b[K     |████████████████████████████████| 85 kB 3.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: transformers<5.0.0,>=4.6.0 in /usr/local/lib/python3.8/dist-packages (from sentence-transformers) (4.25.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from sentence-transformers) (4.64.1)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.8/dist-packages (from sentence-transformers) (1.13.0+cu116)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.8/dist-packages (from sentence-transformers) (0.14.0+cu116)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from sentence-transformers) (1.21.6)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.8/dist-packages (from sentence-transformers) (1.0.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from sentence-transformers) (1.7.3)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.8/dist-packages (from sentence-transformers) (3.7)\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.97-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.3 MB 70.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: huggingface-hub>=0.4.0 in /usr/local/lib/python3.8/dist-packages (from sentence-transformers) (0.11.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (2.23.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (3.8.2)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (4.4.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (6.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.9->huggingface-hub>=0.4.0->sentence-transformers) (3.0.9)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.8/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (0.13.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2022.6.2)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.8/dist-packages (from nltk->sentence-transformers) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.8/dist-packages (from nltk->sentence-transformers) (1.2.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2022.12.7)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn->sentence-transformers) (3.1.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.8/dist-packages (from torchvision->sentence-transformers) (7.1.2)\n",
            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125938 sha256=e1339f5ddd76fe506af458581e08a7cfdc7a885d32892ad9b0f434ea5329dd18\n",
            "  Stored in directory: /root/.cache/pip/wheels/5e/6f/8c/d88aec621f3f542d26fac0342bef5e693335d125f4e54aeffe\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: sentencepiece, sentence-transformers\n",
            "Successfully installed sentence-transformers-2.2.2 sentencepiece-0.1.97\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "model = SentenceTransformer('all-MiniLM-L6-v2')"
      ],
      "metadata": {
        "id": "OqqngIorE0qF",
        "colab": {
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "28557743dd284c289d1c8476088fd363"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/349 [00:00<?, ?B/s]"
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "e941f6af5f33471a9b8d78bf410b6bcd"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "X = np.array(dftr['Verbatim Feedback '])\n",
        "y = dftr['Sentiment (1=Positive & 0= Negative)']"
      ],
      "metadata": {
        "id": "nLXFsDrQE6sL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "lhcyc-SjE8pF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_, X_test_, y_train, y_test = train_test_split(X, y, test_size = 0.2)\n",
        "X_train = model.encode(X_train_)\n",
        "X_test = model.encode(X_test_)"
      ],
      "metadata": {
        "id": "m3-p_ccGFSte"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "WjwQ41d5FwTf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import loadtxt\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "0a1YkQqdHiuW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fit model no training data\n",
        "model = XGBClassifier()\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fDCQfhTDILv4",
        "outputId": "87753703-21b6-4707-cdd6-596cd7fe22e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k5X_z3ySIPq9",
        "outputId": "816949db-6a7e-49bd-826d-4fcb0673fd14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n",
              "       0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0,\n",
              "       1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.array(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gnCczkCHISnD",
        "outputId": "d6bba2c2-f937-46c5-954d-a97fea180159"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0,\n",
              "       0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0,\n",
              "       1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
              "       0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report"
      ],
      "metadata": {
        "id": "rmg-u5S0IUu4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, model.predict(X_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZEExBJX6I2fz",
        "outputId": "c4fdac51-d9a4-40d6-c58f-ede0abe3f299"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.93      0.89        43\n",
            "           1       0.90      0.80      0.85        35\n",
            "\n",
            "    accuracy                           0.87        78\n",
            "   macro avg       0.88      0.87      0.87        78\n",
            "weighted avg       0.87      0.87      0.87        78\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_[9]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "U2ejJurnI6j1",
        "outputId": "4cff9522-d5ca-4a4a-c244-107c8e3d30cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Good quality'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install vaderSentiment"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDHuw8Jkf1lE",
        "outputId": "7f43c8af-9525-4382-d387-88025affc734"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting vaderSentiment\n",
            "  Downloading vaderSentiment-3.3.2-py2.py3-none-any.whl (125 kB)\n",
            "\u001b[K     |████████████████████████████████| 125 kB 8.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from vaderSentiment) (2.23.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->vaderSentiment) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->vaderSentiment) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->vaderSentiment) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->vaderSentiment) (2022.12.7)\n",
            "Installing collected packages: vaderSentiment\n",
            "Successfully installed vaderSentiment-3.3.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#import the library\n",
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
        "\n",
        "#calculate the negative, positive, neutral and compound scores, plus verbal evaluation\n",
        "def sentiment_vader(sentence):\n",
        "\n",
        "    # Create a SentimentIntensityAnalyzer object.\n",
        "    sid_obj = SentimentIntensityAnalyzer()\n",
        "\n",
        "    sentiment_dict = sid_obj.polarity_scores(sentence)\n",
        "    negative = sentiment_dict['neg']\n",
        "    neutral = sentiment_dict['neu']\n",
        "    positive = sentiment_dict['pos']\n",
        "    compound = sentiment_dict['compound']\n",
        "\n",
        "    if sentiment_dict['compound'] >= 0.05 :\n",
        "        overall_sentiment = 1\n",
        "\n",
        "    elif sentiment_dict['compound'] <= - 0.05 :\n",
        "        overall_sentiment = -1\n",
        "\n",
        "    else :\n",
        "        overall_sentiment = 0\n",
        "  \n",
        "    return negative, neutral, positive, compound, overall_sentiment"
      ],
      "metadata": {
        "id": "hCEZISrXJ2d4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dftr['Verbatim Feedback '].apply(lambda x:sentiment_vader(x))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x1wVvpaUfx1J",
        "outputId": "1ca67f3d-1836-4829-c093-1b333f344038"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       (0.355, 0.645, 0.0, -0.296, -1)\n",
              "1               (0.0, 1.0, 0.0, 0.0, 0)\n",
              "2               (0.0, 1.0, 0.0, 0.0, 0)\n",
              "3       (0.688, 0.312, 0.0, -0.296, -1)\n",
              "4               (0.0, 1.0, 0.0, 0.0, 0)\n",
              "                     ...               \n",
              "382             (0.0, 1.0, 0.0, 0.0, 0)\n",
              "383             (0.0, 1.0, 0.0, 0.0, 0)\n",
              "384             (0.0, 1.0, 0.0, 0.0, 0)\n",
              "385    (0.365, 0.635, 0.0, -0.3182, -1)\n",
              "386             (0.0, 1.0, 0.0, 0.0, 0)\n",
              "Name: Verbatim Feedback , Length: 387, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "CHpGLljLf8bO",
        "outputId": "2f5ea523-a2f8-46fe-c40d-b182fb349b76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Product                               Job Role  \\\n",
              "0          IX                                  XYZ   \n",
              "1          IX                            Architect   \n",
              "2          IX                                  PQR   \n",
              "3          IX                     Sales Specialist   \n",
              "4          IX                            Architect   \n",
              "..        ...                                  ...   \n",
              "382        QK  TECHNICAL LEADER.CUSTOMER Executive   \n",
              "383        QK          Engineer - Customer Support   \n",
              "384        QK  TECHNICAL LEADER.CUSTOMER Executive   \n",
              "385        QK           Support.CUSTOMER Executive   \n",
              "386        QK  TECHNICAL LEADER.CUSTOMER Executive   \n",
              "\n",
              "                                    Verbatim Feedback   \\\n",
              "0                        There is no  maintenance menu   \n",
              "1                                          Very buggy.   \n",
              "2                            High quality -  standards   \n",
              "3                                     Features missing   \n",
              "4                          how to do the inter-working   \n",
              "..                                                 ...   \n",
              "382                                  uninteresting GUI   \n",
              "383                                  UI is interactive   \n",
              "384  Totally instable.. Enginieering quallity has d...   \n",
              "385                           Lack of work around this   \n",
              "386  The platform is been wide deploy This trigger ...   \n",
              "\n",
              "     Sentiment (1=Positive & 0= Negative)  \n",
              "0                                       0  \n",
              "1                                       0  \n",
              "2                                       1  \n",
              "3                                       0  \n",
              "4                                       0  \n",
              "..                                    ...  \n",
              "382                                     0  \n",
              "383                                     1  \n",
              "384                                     0  \n",
              "385                                     0  \n",
              "386                                     0  \n",
              "\n",
              "[387 rows x 4 columns]"
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              "      <th>385</th>\n",
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              "      <td>QK</td>\n",
              "      <td>TECHNICAL LEADER.CUSTOMER Executive</td>\n",
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              "<p>387 rows × 4 columns</p>\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-3246bcfd-fa4f-4c75-88da-7f565f59e842 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3246bcfd-fa4f-4c75-88da-7f565f59e842');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "GlqlbM5agMUd",
        "outputId": "30b7b55e-6e6f-4652-9ffb-c705432e27b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-60-43a58eb494e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/xgboost/sklearn.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, data, output_margin, ntree_limit, validate_features)\u001b[0m\n\u001b[1;32m    783\u001b[0m         \u001b[0mprediction\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    784\u001b[0m         \"\"\"\n\u001b[0;32m--> 785\u001b[0;31m         \u001b[0mtest_dmatrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissing\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnthread\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    786\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mntree_limit\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    787\u001b[0m             \u001b[0mntree_limit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"best_ntree_limit\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, label, missing, weight, silent, feature_names, feature_types, nthread)\u001b[0m\n\u001b[1;32m    402\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_from_csc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    403\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 404\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_from_npy2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnthread\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    405\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataTable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_from_dt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnthread\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36m_init_from_npy2d\u001b[0;34m(self, mat, missing, nthread)\u001b[0m\n\u001b[1;32m    472\u001b[0m         \"\"\"\n\u001b[1;32m    473\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 474\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Input numpy.ndarray must be 2 dimensional'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    475\u001b[0m         \u001b[0;31m# flatten the array by rows and ensure it is float32.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m         \u001b[0;31m# we try to avoid data copies if possible (reshape returns a view when possible\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input numpy.ndarray must be 2 dimensional"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install happytransformer"
      ],
      "metadata": {
        "id": "70fmbLUGgVjP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "14fecf9d-519c-4b28-8d55-8b9ff99ec454"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting happytransformer\n",
            "  Downloading happytransformer-2.4.1-py3-none-any.whl (45 kB)\n",
            "\u001b[?25l\r\u001b[K     |███████▏                        | 10 kB 18.1 MB/s eta 0:00:01\r\u001b[K     |██████████████▍                 | 20 kB 25.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▋          | 30 kB 18.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▉   | 40 kB 10.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 45 kB 3.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: transformers>=4.4.0 in /usr/local/lib/python3.8/dist-packages (from happytransformer) (4.25.1)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.8/dist-packages (from happytransformer) (3.19.6)\n",
            "Requirement already satisfied: tqdm>=4.43 in /usr/local/lib/python3.8/dist-packages (from happytransformer) (4.64.1)\n",
            "Collecting datasets>=1.6.0\n",
            "  Downloading datasets-2.8.0-py3-none-any.whl (452 kB)\n",
            "\u001b[K     |████████████████████████████████| 452 kB 49.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: sentencepiece in /usr/local/lib/python3.8/dist-packages (from happytransformer) (0.1.97)\n",
            "Requirement already satisfied: torch>=1.0 in /usr/local/lib/python3.8/dist-packages (from happytransformer) (1.13.0+cu116)\n",
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            "Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from datasets>=1.6.0->happytransformer) (21.3)\n",
            "Collecting xxhash\n",
            "  Downloading xxhash-3.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (213 kB)\n",
            "\u001b[K     |████████████████████████████████| 213 kB 82.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: huggingface-hub<1.0.0,>=0.2.0 in /usr/local/lib/python3.8/dist-packages (from datasets>=1.6.0->happytransformer) (0.11.1)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.8/dist-packages (from datasets>=1.6.0->happytransformer) (2.23.0)\n",
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            "Requirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.8/dist-packages (from datasets>=1.6.0->happytransformer) (9.0.0)\n",
            "Requirement already satisfied: dill<0.3.7 in /usr/local/lib/python3.8/dist-packages (from datasets>=1.6.0->happytransformer) (0.3.6)\n",
            "Collecting multiprocess\n",
            "  Downloading multiprocess-0.70.14-py38-none-any.whl (132 kB)\n",
            "\u001b[K     |████████████████████████████████| 132 kB 77.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: aiohttp in /usr/local/lib/python3.8/dist-packages (from datasets>=1.6.0->happytransformer) (3.8.3)\n",
            "Collecting responses<0.19\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from datasets>=1.6.0->happytransformer) (1.21.6)\n",
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            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0.0,>=0.2.0->datasets>=1.6.0->happytransformer) (4.4.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0.0,>=0.2.0->datasets>=1.6.0->happytransformer) (3.8.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging->datasets>=1.6.0->happytransformer) (3.0.9)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets>=1.6.0->happytransformer) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets>=1.6.0->happytransformer) (2022.12.7)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets>=1.6.0->happytransformer) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets>=1.6.0->happytransformer) (3.0.4)\n",
            "Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
            "  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
            "\u001b[K     |████████████████████████████████| 127 kB 74.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.8/dist-packages (from transformers>=4.4.0->happytransformer) (0.13.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers>=4.4.0->happytransformer) (2022.6.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets>=1.6.0->happytransformer) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets>=1.6.0->happytransformer) (2022.6)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->datasets>=1.6.0->happytransformer) (1.15.0)\n",
            "Installing collected packages: urllib3, xxhash, responses, multiprocess, datasets, happytransformer\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "Successfully installed datasets-2.8.0 happytransformer-2.4.1 multiprocess-0.70.14 responses-0.18.0 urllib3-1.25.11 xxhash-3.2.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "urllib3"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from happytransformer import HappyTextClassification"
      ],
      "metadata": {
        "id": "qIOSS_9piwe8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "happy_tc = HappyTextClassification(model_type=\"DISTILBERT\", model_name=\"distilbert-base-uncased-finetuned-sst-2-english\", num_labels=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "6a01ed5f16c645e39ade60a4b0c22d78",
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            "d7e52ed3e43a4a90b2d6e81e427f3f83"
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        },
        "id": "N_oN3w3Mi7c8",
        "outputId": "825eff0c-d031-4002-85e1-2e99e15fc5a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/629 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6a01ed5f16c645e39ade60a4b0c22d78"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f1a3d39d82b74d2493d065ebffad197f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0ed74c1d7bcb4ad2b5a28e77f8a4e087"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ca853fc32cd646f2b924c69fe2f4541c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = happy_tc.classify_text(\"hello\")"
      ],
      "metadata": {
        "id": "LQ8PFvpmjVRD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result.label"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "qWXFcczojjFE",
        "outputId": "31df349b-951f-4c13-8524-43234c28c84d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'POSITIVE'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftr['prd'] = dftr['Verbatim Feedback '].apply(happy_tc.classify_text)"
      ],
      "metadata": {
        "id": "gctf4i0Mjktp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dftr['prd'] = dftr['prd'].apply(lambda x:1 if x.label[0]=='P' else 0)"
      ],
      "metadata": {
        "id": "WCDMkxTtjpPE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K0gdZ6zdkKNK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(dftr['Sentiment (1=Positive & 0= Negative)'], dftr['prd']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jQvfX64QkXvz",
        "outputId": "935e2529-6836-40e5-a8d0-deb40f0c0e92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.90      0.86       213\n",
            "           1       0.86      0.78      0.82       174\n",
            "\n",
            "    accuracy                           0.84       387\n",
            "   macro avg       0.85      0.84      0.84       387\n",
            "weighted avg       0.84      0.84      0.84       387\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "st = ('. '.join(dftr[(dftr['prd']==0) & (dftr['Product  ']=='IX') & (dftr['Job Role']=='Support')]['Verbatim Feedback ']))"
      ],
      "metadata": {
        "id": "Kw4wGaMDklQg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install matplotlib\n",
        "!pip install pandas\n",
        "!pip install wordcloud"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wi7vTa7uno1q",
        "outputId": "d1756b83-de38-499b-b695-53e63d3491cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (3.2.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (3.0.9)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (1.21.6)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (1.4.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.1->matplotlib) (1.15.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (1.3.5)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2022.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (1.21.6)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: wordcloud in /usr/local/lib/python3.8/dist-packages (1.8.2.2)\n",
            "Requirement already satisfied: numpy>=1.6.1 in /usr/local/lib/python3.8/dist-packages (from wordcloud) (1.21.6)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (from wordcloud) (3.2.2)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.8/dist-packages (from wordcloud) (7.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->wordcloud) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib->wordcloud) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->wordcloud) (1.4.4)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->wordcloud) (3.0.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.1->matplotlib->wordcloud) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from wordcloud import WordCloud, STOPWORDS\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "stopwords = set(STOPWORDS)\n",
        "wordcloud = WordCloud(width = 800, height = 800,\n",
        "                background_color ='white',\n",
        "                stopwords = stopwords,\n",
        "                min_font_size = 10).generate(st)\n",
        "plt.figure(figsize = (8, 8), facecolor = None)\n",
        "plt.imshow(wordcloud)\n",
        "plt.axis(\"off\")\n",
        "plt.tight_layout(pad = 0)\n",
        " \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "-z8loWv2siSG",
        "outputId": "b2ea516c-b8bf-4271-bc29-7df180c5914c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "st"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "id": "tdDpHxaSv-Vq",
        "outputId": "7b5221fc-2bbe-4041-8a57-2449a32d1d6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The problem has always been constant..  Some ongoing issues so far.Few issues reported so far on customer.. There are many bugs that make it difficult to determine whether it is software or hardware. lacks of documentation of unSupported features and features combinations. . We found a HW issue. Could be SW.There is a lot of doc. It would be good to centralized.. needs to test this boxes properly. Very poor drop accounting and troubleshooting capabilities.  Support is pretty much guess work and relying on other boxes in the network to provide the missing clues.. good product with enhanced Architecture , but this product also needs time to become as stable. I think there are still bugs that can only be considered lack of testing. Both software and hardware should be  improve..  defective hardware. not many issue observednew product not seen much used by customer.  So many feature and command are not Support making it a very difficult product to troubleshoot. Lack of serviceability and reliability. It looks like there are too many bugs. thoroughly.Anyway, there are many problems and the quality is only unsuitable'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bert_summary = ''.join(bert_model(st, min_length=10))\n",
        "print(bert_summary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HQ21Jnv3wsQY",
        "outputId": "04a238e8-cdf9-4f00-e5b8-f84a32f3f364"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The problem has always been constant..  Some ongoing issues so far. Support is pretty much guess work and relying on other boxes in the network to provide the missing clues.. good product with enhanced Architecture , but this product also needs time to become as stable. Both software and hardware should be  improve..  defective hardware. Lack of serviceability and reliability.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "st = ''.join(st.split('. '))"
      ],
      "metadata": {
        "id": "5UPBud8UyXRy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bert_summary = ' '.join(bert_model(st, min_length=10))\n",
        "print(bert_summary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "flJNk8F2y_4N",
        "outputId": "9cef735d-28ba-4563-85b5-902e08703aa7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T h e   p r o b l e m   h a s   a l w a y s   b e e n   c o n s t a n t .   d e f e c t i v e   h a r d w a r e n o t   m a n y   i s s u e   o b s e r v e d n e w   p r o d u c t   n o t   s e e n   m u c h   u s e d   b y   c u s t o m e r   S o   m a n y   f e a t u r e   a n d   c o m m a n d   a r e   n o t   S u p p o r t   m a k i n g   i t   a   v e r y   d i f f i c u l t   p r o d u c t   t o   t r o u b l e s h o o t L a c k   o f   s e r v i c e a b i l i t y   a n d   r e l i a b i l i t y I t   l o o k s   l i k e   t h e r e   a r e   t o o   m a n y   b u g s t h o r o u g h l y .\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "RATINGS\n"
      ],
      "metadata": {
        "id": "K-3-g6bIRTme"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.tokenize import word_tokenize, RegexpTokenizer\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer \n",
        "  \n",
        "# function to print sentiments \n",
        "# of the sentence. \n",
        "def sentiment_scores(sentence): \n",
        "  \n",
        "    # Create a SentimentIntensityAnalyzer object. \n",
        "    sid_obj = SentimentIntensityAnalyzer() \n",
        "  \n",
        "    # polarity_scores method of SentimentIntensityAnalyzer \n",
        "    # oject gives a sentiment dictionary. \n",
        "    # which contains pos, neg, neu, and compound scores. \n",
        "    sentiment_dict = sid_obj.polarity_scores(sentence)  \n",
        "    return sentiment_dict['compound']"
      ],
      "metadata": {
        "id": "h69IJy9RzL92"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dftr['Verbatim Feedback '].apply(lambda x:sentiment_scores(x))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P9El0N3vLtr9",
        "outputId": "9fba9adc-5f21-44fd-c76f-eccae1e4b82b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     -0.2960\n",
              "1      0.0000\n",
              "2      0.0000\n",
              "3     -0.2960\n",
              "4      0.0000\n",
              "        ...  \n",
              "382    0.0000\n",
              "383    0.0000\n",
              "384    0.0000\n",
              "385   -0.3182\n",
              "386    0.0000\n",
              "Name: Verbatim Feedback , Length: 387, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# d=dftr[dftr['Job Role'] == 'Sales Specialist']\n",
        "# s=d['Verbatim Feedback '].apply(lambda x:sentiment_scores(x))\n",
        "# z=s.reset_index(level=None, drop=True, name=None, inplace=False)\n",
        "# rating=0\n",
        "# for i in range(z.count()):\n",
        "#   rating=rating+z[i]\n",
        "# rating =rating/z.count()\n",
        "# rating*5\n",
        "\n",
        "# ## Lowest rating 0 and highest rating 5 ##"
      ],
      "metadata": {
        "id": "1HS6DBSaJ1tQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "m=dft['Job Role'].unique()\n",
        "m[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "tCzvQjxVM4t0",
        "outputId": "7c8c66a8-b13e-4be4-ed27-2c26270ff665"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Operations'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m=dft['Job Role'].unique()\n",
        "for name in m:\n",
        "  d=dft[dft['Job Role'] == name]\n",
        "  s=d['Verbatim Feedback '].apply(lambda x:sentiment_scores(x))\n",
        "  z=s.reset_index(level=None, drop=True, name=None, inplace=False)\n",
        "  rating=0\n",
        "  for i in range(z.count()):\n",
        "    rating=rating+z[i]\n",
        "  round_rating=round(rating,2)\n",
        "  if z.count()>0:\n",
        "    rating =rating/z.count()\n",
        "    norm_rate=((rating+1)/2)*5\n",
        "    print(name, \": \", norm_rate)\n",
        "  else:\n",
        "    print(name,\": \", 2.5)\n",
        "\n",
        "## Lowest rating 0 and highest rating 5 ##"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cNYzbvEMNEVM",
        "outputId": "15ef08dd-858e-4fea-9abe-e95c8aa625b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Operations :  2.5396\n",
            "Architect :  2.4968629032258063\n",
            "Sales Specialist :  3.4439583333333337\n",
            "Systems Engineer :  3.14071875\n",
            "Solutions :  3.0776\n",
            "PrincipalabcArchitect :  3.0334166666666667\n",
            "PQR :  2.5637916666666665\n",
            "XYZ :  2.544525\n",
            "Support :  2.7067727272727273\n",
            "Engineer - Consultant [C] :  2.7005625\n",
            "Manager :  2.6000833333333335\n",
            "Engineer :  2.8315625\n",
            "Leader, Regionalabc :  2.7043749999999998\n",
            "Sales Business Development Manager :  2.5\n",
            "Leader, Client Executive :  4.09225\n",
            "Business SolutionsArchitect :  2.7567500000000003\n",
            "Leader, Customer Executive :  1.1936250000000002\n",
            "SecurityCustomer Success Executive :  2.5\n",
            "High Touch Operations Manager :  1.9313749999999998\n",
            "CXO :  3.6917500000000003\n",
            "Customer Service & Support [C] :  1.353\n"
          ]
        }
      ]
    },
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      "metadata": {
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      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W1ENQBiUTpP7"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}