social-sentiment-analyser
This project is useful for social media users, content creators, marketers, and businesses who want to understand public sentiment. They can analyze Hinglish and English posts, identify positive, negative, or neutral opinions, and see which words or emojis influenced each prediction.
View arpitaengineer08-source/social-sentiment-analyser on GitHub- GitHub stars
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Project details
- Repository
- arpitaengineer08-source/social-sentiment-analyser
- Licence
- MIT
- Catalog status
- Available
- Catalog check
- 2026-11-13
- Required costs
- No additional costs or paid subscriptions are required. Users only need an internet connection to access the project. The tool is free to use under the MIT License.
Repository description
End-to-end machine learning project that classifies social media posts as positive, neutral or negative. It includes a social-media-aware text pipeline, automatic model comparison and tuning, a CLI, a Streamlit dashboard with batch scoring, per-prediction explanations and a test suite. Highlights Social-media-aware preprocessing (src/preprocessing.py): URLs, @mentions, hashtags, emojis, elongated words (soooo), contractions, negation scope handling (not good -> NOT_good) and Hinglish slang. No NLTK downloads needed. Model selection done properly (src/train.py): Logistic Regression, Linear SVM and Complement NB, each tuned with GridSearchCV (stratified CV, macro-F1). The winner is chosen on cross-validation, not the test set, so the test score stays an honest estimate. Explainable: every prediction shows the words that pushed it towards the predicted class. Three ways to use it: Python API, command line, Streamlit dashboard (single post + CSV batch + insights). Zero setup friction: the dataset ships with the project and the model trains itself on first run.
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