datascience

Nishtha Grover/ProductReviewSentimentalAnalysisDashboard/app.py

@@ -0,0 +1,187 @@
+from flask import Flask, request, render_template, url_for, redirect, flash, Response
+from bs4 import BeautifulSoup
+import pandas as pd
+import requests
+import re
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+import pygal
+from pygal.style import BlueStyle
+from pygal.style import LightGreenStyle
+from pygal.style import Style
+
+app = Flask(__name__)
+app.jinja_env.add_extension('jinja2.ext.loopcontrols')
+product_dict = {}
+product_links = []
+reviews_list = []
+img_dict = {}
+totalRating = ''
+avgRating = ''
+
+@app.route('/', methods=['GET', 'POST'])
+def home():
+	if request.method == 'POST':
+		search_query = request.form['words']
+		url = get_search_item(search_query)
+		getProductdictionary(url)
+		getProductLinks(url)
+		return redirect(url_for('getProductList'))	
+	return render_template("home.html")
+
+@app.route('/ProductList',methods=['GET', 'POST'])
+def getProductList():
+	global product_dict
+	global review_list
+	global img_dict
+	if request.method == 'POST':
+		product_id = int(request.form['product_id'])
+		asin = getAsin(product_links[product_id - 1])
+		reviewList(reviewLink(str(asin)))	
+		return redirect(url_for('getReviewAnalysis'))
+	return render_template("productList.html",products=product_dict,images=img_dict)
+
+@app.route('/ReviewAnalysis')
+def getReviewAnalysis():
+	reviews = sentiment_scores()
+
+
+	custom_style = Style(
+	background='transparent',
+	plot_background='transparent',
+	foreground='#53E89B',
+	foreground_strong='#53A0E8',
+	foreground_subtle='#630C0D',
+	opacity='.7',
+	opacity_hover='.99',
+	transition='400ms ease-in',
+	colors=('#ff1100', '#ffee00', '#0000ff'))
+
+	# graph = pygal.Bar(fill = True, style=BlueStyle)
+	graph = pygal.Bar(fill=True, interpolate='cubic', style=BlueStyle)
+	graph.title = "Sentiment of the Users' Reviews"
+	graph.x_labels = ["positive", "negative", "neutral"]
+	graph.add("%age of Reviews",reviews)
+	graph_data = graph.render_data_uri()
+
+
+
+
+
+	pie = pygal.Pie(fill=True, interpolate='cubic', style=LightGreenStyle)
+	pie.title = "Sentiment of the Users' Reviews"
+	pie.add('positive', reviews[0])
+	pie.add('negative', reviews[1])
+	pie.add('neutral', reviews[2])
+	pie_data = pie.render_data_uri()
+
+	global totalRating
+	global avgRating
+	return render_template("ReviewAnalysis.html",graph_data=graph_data, pie_data=pie_data, total=totalRating, avg=avgRating)
+
+def sentiment_scores(): 
+	global reviews_list
+	count, positive, negative, neutral = 0, 0, 0, 0
+	for review in reviews_list:
+	    sentiment_obj = SentimentIntensityAnalyzer()  
+	    sentiment_dict = sentiment_obj.polarity_scores(review) 
+
+	    if sentiment_dict['compound'] >= 0.05 : 
+	        positive +=1
+
+	    elif sentiment_dict['compound'] <= - 0.05 : 
+	        negative +=1
+
+	    else : 
+	        neutral+=1    
+	pos = (positive/len(reviews_list))*100
+	neg = (negative/len(reviews_list))*100
+	neu = (neutral/len(reviews_list))*100
+	reviews = [pos,neg,neu]
+	return reviews
+
+
+def getAmazonSearch(url):
+    header = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.90 Safari/537.36'}
+    page = requests.get(url, headers = header)
+    if page.status_code == 200:
+        return page
+    else:
+        return "Error"
+
+def getAsin(selected_product_link): 
+    x = selected_product_link.index("B0")
+    asin = selected_product_link[x:x+10]
+    return asin
+
+def reviewLink(asin):
+	header = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.90 Safari/537.36'}
+	asin_url = "https://www.amazon.in/dp/" + asin
+	page = requests.get(asin_url, headers = header)
+	page = page.text
+	soup = BeautifulSoup(page)
+	x = soup.find("a", {'class':'a-link-emphasis a-text-bold'})
+	return 'https://www.amazon.in/' + str(x['href'])
+
+def reviewList(url):
+	header = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.90 Safari/537.36'}
+	global reviews_list
+	global totalRating
+	global avgRating
+	page = requests.get(url, headers = header)
+	page = page.text
+	soup = BeautifulSoup(page)
+
+	for i in soup.findAll("span", {'data-hook':"review-body"}):
+	    reviews_list.append(i.text)
+
+	y = soup.find("span", {"class": "a-size-base a-color-secondary"})
+	totalRating =  y.text.split()[0]
+
+	x = soup.find("span", {"data-hook": "rating-out-of-text"})
+	avgRating = x.text
+
+def get_search_item(search_query):
+	search_query = search_query.replace(' ', '+')
+	base_url = "https://www.amazon.in/s?k="
+	url = base_url + search_query
+	return (url)
+
+def getProductdictionary(url):
+	header = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.90 Safari/537.36'}
+	search_response = requests.get(url, headers=header)
+
+	global product_dict
+	product_names=[]
+	response = getAmazonSearch(url)
+	soup = BeautifulSoup(response.content)
+	for i in soup.findAll("span", {'class':'a-size-base-plus a-color-base a-text-normal'}): 
+	    product_names.append(i.text)
+	    if len(product_names) == 6:
+	        break
+
+	for i, j in enumerate(product_names):
+	    product_dict[i+1] = j
+
+	image_info = soup.select('.s-image')
+	for i, j in enumerate(image_info):
+	    img_dict[i+1] = j['src']
+
+def getProductLinks(url):
+	header = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.90 Safari/537.36'}
+	search_response = requests.get(url, headers=header)
+
+	global product_links
+	global img_list
+	page = requests.get(url)
+	page = page.text
+	soup = BeautifulSoup(search_response.content)
+
+	for i in soup.findAll("a",{'class':'a-link-normal a-text-normal'}):
+	    product_links.append(i['href'])
+
+	# image_info = soup.select('.s-image')
+	# for i, j in enumerate(image_info):
+	#     img_dict[i+1] = j['src']
+
+if __name__ == '__main__':
+    app.run(host='127.0.0.1',debug=True)