NUS Sentiment is a sentiment checker which takes in keyword(s) and scrapes r/NUS for posts and comments containing the keyword(s). It allows NUS students to learn others' opinions of NUS-related topics, such as modules, professors, and accommodation, easier.
The posts and comments containing specific keyword(s) are run through a Sentiment Analysis Pipeline, which helps gauge people's sentiment toward the keyword(s). These results are collected, and a quick summary is displayed via various data visualization methods. They can also be pushed to a vector database where in the SemanticSearch page of the web app, users can make natural language queries. Top ranked matches by cosine similarity will be returned in a user-friendly format.
This project was done within the 24-hour limit of NUS Hack&Roll 2023 and won Coreteam's Best Roll.
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Model: The main model we used to predict the sentiment of post/comment is a pre-trained
cardiffnlp/twitter-roberta-base-sentimentfrom HuggingFace Transformers. Pre-processing tokens for our WordCloud was done withTextBlob -
Data: All data used is scraped from r/NUS using
PRAW -
Charts: Altair, Matplotlib, Plotly Express
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Database: We used Pinecone, a vector database to power our semantic search engine.Deta Base, a key-value store was also used to keep track of the most common keywords searched.
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Frontend: Streamlit
git clone [email protected]:nus-sentiment/nus-sentiment.git
cd nus-sentiment
virtualenv venv
source venv/bin/activate
pip install -r requirements.txt
streamlit run Search.py