College-Sentiment-Analysis

Sentiment Analysis based on College reviews using ML models and Django

ABOUT THE PROJECT: Education is a crucial decision, and choosing the right college shapes a student’s future. With countless options, students and parents rely on online reviews, but manually analyzing them is time-consuming. A Sentiment Analysis model can automate this process by analyzing reviews and categorizing them as positive ,negative or neutral, helping students get a quick and clear understanding of a college’s reputation

The project aims to develop a Machine Learning-based Sentiment Analysis model that can:

1. Accept the name of a college and review as input from users. Classify the sentiment of these reviews as positive, negative or neutral and give as output.

2. Accept the name of a college as input from users and give the college status based on reviews as positive, negative or neutral.

Technologies used For ML model: BERT [since based on NLP] For Webapp: HTML,CSS,Django,Python

Run on your local machine by: git clone https://github.com/siddhali24/College-Sentiment-Analysis.git

python manage.py runserver

then go to port http://127.0.0.1:8000/ to run the local server

How to Set Up Model Files

The machine learning model files (Naive Bayes or BERT) are not included in the main repo due to size limits. They are available for download from the Releases page of this repository:

👉 [Download Model Files from Releases]https://github.com/siddhali24/College-Sentiment-Analysis.git/releases/

After Downloading

1. Extract the downloaded .zip.

2. Place the files as follows:

sentiment/analysis/ml_model/bert_model/

also

ml_model/balanced_reviews(1).csv

ensure bert_model folder has following:

• config.json
• model.safetensors
• special_tokens_map.json
• tokenizer_config.json
• vocab.txt

Important Notes

• Make sure the files are placed exactly in the above folders, as the code depends on these paths.
• If you change folder names, update the paths in your Django views accordingly.
• After placing the files, run your Django server as usual.