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The Problem

Before starting a business, business makers scope out problems in order to provide products to untapped markets. Before starting a hackathon, team members similarly scope out problems that inspire their technical solutions. Our problem? We couldn't find a problem!

Introducing Sitegeist, our technical solution for giving you more problems! We wanted a way to easily analyze trending topics and issues faced by those around us. Sitegeist uses machine learning to predict the sentiment held behind every sentence (literally, every sentence) within a subreddit, giving you trending keywords, their associated sentiments, as well as example posts that give direct examples of those dealing with such topics and issues. Finally, we utilize the power of ChatGPT to additionally provide the user with ideas towards a solution to solve or expand the trending topics.

What We Learned

Some things we learned during the project is the bottleneck produced by needing a backend server to properly continue development on the frontside. We dealt with this by designing our database schema and API designs early on in order for us to be able to work with consistent formats within the dummy data used for fundamental testing.

Working in parallel with others provides many advantages, however, overlapping efforts was a particularly difficult challenge for us. Oftentimes working within the same file, most merges resulted in conflicts that needed to be dealt with. However, we learned to strategize the timings of our efforts by making sure the efforts of an individual are towards a different enough piece of the project than the rest of the members.

Built With

• chart.js
• chartjs-plugin-annotation
• chatgptapi
• eslint
• fastapi
• javascript
• lottie-react
• mui
• nextjs
• nltk
• openaiapi
• pandas
• praw
• prettier
• python
• react
• reddit
• render
• scikit-learn
• spacy
• sqlalchemy
• sqlite
• textblob
• transformers
• uvicorn
• vercel