Sentiment Index

Inspiration

The abundance of contradictory articles on the internet which skew perception of the overall market

What it does

Gives a reading of the current market sentiment based on news articles and Reddit posts from the r/stocks subreddit.

How we built it

Technology

• Used Cohere to build a classification model trained on news article headlines and Reddit posts, to analyze the sentiment of the current market.
• Used Redis to manage the job queue for jobs that run the classification algorithm.
• Used Netlify to host the frontend
• Used Heroku to host the backend

Backend

• Flask

Frontend

• React with Typescript
• Used ChartJS to create the graph that showcases the sentiment changes over the past week.

Database

• MongoDB

APIs

• Reddit API to aggregate posts from reddit.
• Free News API by Newscatcher API to aggregate news articles.

Challenges we ran into

• Since we used a lot of different technologies for the first time, we had to allocate more of our time to learning them.
• We also had to spend time to fine tune the classification model so that it can correctly identify the sentiment based on the news article or Reddit post.

Accomplishments that we're proud of

We are proud that we generated accurate sentiment ratings for the news articles and Reddit posts.

What we learned

We learned how to use Redis to schedule the time-consuming classification jobs, so that it runs in the background.

What's next for Sentiment Index

We want to add the ability to generate sentiment analysis of individual stocks.

Built With

• chartjs
• cohere
• flask
• heroku
• mongodb
• netlify
• python
• react
• redis
• typescript