galafis/NLP-Sentiment-Analyzer
Sentiment analysis with TextBlob and TF-IDF + Logistic Regression, Flask web UI — Python
What's novel
Sentiment analysis with TextBlob and TF-IDF + Logistic Regression, Flask web UI — Python
Code Analysis
4 files read · 2 roundsA Flask web app that classifies English text sentiment using either a TF-IDF + Logistic Regression model (trained on 10 sample sentences) or a TextBlob lexicon fallback, with a single-page HTML UI for single and batch analysis.
Strengths
Clean, readable code with a well-organized test suite covering preprocessing, sentiment classification, and batch operations. The dual-method fallback (trained model → TextBlob) is a sensible design choice for a demo.
Weaknesses
The 'custom model' is trained on only 10 hardcoded sample sentences, making it a toy example rather than a real ML pipeline. The entire application (NLP logic, Flask routes, HTML template, sample data, main entry) is crammed into a single 12.9KB file with no separation of concerns, and error handling is minimal (bare except in load_model, no input validation beyond empty checks).
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
9
Contributors
1
Files
8
Active weeks
3
Repository
Language
Python
Stars
1
Forks
0
License
MIT