Project Overview
This web application provides real-time sentiment analysis of the 50 most recent posts from r/politics and r/AITA, and will update based on new posts.
Backend: Flask, Textblob for sentiment analysis; Front-end: HTML/CSS/JS (formerly TS, which is unecessarily difficult as flask is used with a very specific HTML/CSS/JS structure, and requires many additional dependencies and etc to replace with TS)
Structure
- Displays mean and median sentiment statistics and formatted data of the 50 posts.
- Sentiment ranges from -1 (negative) to 1 (positive)
Coolest Features
- New subreddit pages are literally added by just adding onto the HTML nav bar, due to the "subreddit.html" template page that takes the subreddit as an input from the link.
- See more subreddits by adding the name after the slash ie:
https://quiet-sierra-95106-5ca898adfa7b.herokuapp.com/pics <- r/pics
To do
- Optimizing / analyzing the sentiment analysis model. Looking at the model architecture specifics, epoch opt., confusion matrix, etc - obviously rn still pretty inaccurate but the structure is in place.
- Automate optimization process (ie vocab size, other stuff, stop when validation loss increases)
- Adding user-input for subreddits instead of manually adding on my end
Creator(s)
- Alissa Wu, Duke University Computer Science and Mathematics
- Evan Wang, New York University Mathematics and Computer Science
- PS: This site costs me $7/month to host, support if you want: kofi :)