This is the repository your team will collaborate on and upload code progress to. While it is managed by your team, each code "Merge Request" (aka Pull Request PR)to mast will need to be approved by at least two teammates to be approved.
Outline created by Robert
Disclaimer: I have little-to-no experience with AI/ML so I will here learning along with you 🙂
Sentiment analysis is a machine learning tool that analyzes texts for polarity, from positive to negative. By training machine learning tools with examples of emotions in text, machines automatically learn how to detect sentiment without human input.
To put it simply, machine learning allows computers to learn new tasks without being expressly programmed to perform them. Sentiment analysis models can be trained to read beyond mere definitions, to understand things like, context, sarcasm, and misapplied words. For example:
“Super user-friendly interface. Yeah right. An engineering degree would be helpful.”
Out of context, the words ‘super user-friendly’ and ‘helpful’ could be read as positive, but this is clearly a negative comment. Using sentiment analysis, computers can automatically process text data and understand it just as a human would, saving hundreds of employee hours.
From a high-level, our goal is to create a functional web app that can be used by anyone to evaluate sentiments of specific things from websites or social media sites.
Examples: YouTube video comments, Twitter threads, Reddit posts, etc
Continuing on from the Vision section.
The web app will need to have the following basic functionalities at minimum:
- Text input (link and/or single line statement)
- Web scraper/parser
- Sentiment analysis implementation
The basic flow from a user's perspective will be as follows:
Enter Input → Receive % values for positive, negative, etc → repeat
- Python
- Django
- Firebase [TBD]
- Google Cloud Services [TBD]
[more TBD as needed]
If time permits, we can work on some cool features that will give the project some extra kick!
-
Share functionality
Allow people to share to their choice of social media the results they found through our web app
-
Better styling
We are not going to focus on UI too much at first, so this is defiantly something we can come back to