{"data":{"full_name":"18mahi/tweet-sentiment-analysis","name":"tweet-sentiment-analysis","description":"Classify tweets into happy, sad, angry, excited, and neutral with this interactive Python model. Combining TF-IDF text features with engineered numeric features like emoji sentiment, polarity, subjectivity, and punctuation counts, it demonstrates intermediate-level NLP, feature engineering, and visualization skills.","stars":0.0,"forks":0.0,"language":"Jupyter Notebook","license":"MIT","archived":0.0,"subcategory":"twitter-sentiment-analysis","last_pushed_at":"2025-10-04T16:15:37+00:00","pypi_package":null,"npm_package":null,"downloads_monthly":0.0,"dependency_count":0.0,"commits_30d":null,"reverse_dep_count":0.0,"maintenance_score":2.0,"adoption_score":0.0,"maturity_score":9.0,"community_score":0.0,"quality_score":11.0,"quality_tier":"experimental","risk_flags":"['stale_6m', 'no_package', 'no_dependents']"},"meta":{"timestamp":"2026-04-05T21:26:50.468039+00:00"}}