README.md

metadata

title: SentimentLens
emoji: 🧠
colorFrom: indigo
colorTo: purple
sdk: docker
app_file: app.py
pinned: false

🧠 SentimentLens — TF-IDF vs Fine-tuned DistilBERT

NLP research project: Systematically comparing classical ML against transformer models on Amazon product reviews, with failure analysis.

Built for: Amazon ML Summer School Application
Stack: Python · FastAPI · HuggingFace Transformers · Scikit-learn · React · Vite

---

📊 Results

Model	Accuracy	F1 Score
TF-IDF + Logistic Regression	89.35%	0.90
Fine-tuned DistilBERT	91.70%	0.92

---

🔍 Key Research Finding

Mixed-opinion reviews remain unsolvable under binary classification.
Both models perform at ~45% accuracy on reviews containing both positive and negative aspects — suggesting binary sentiment labels are fundamentally insufficient for nuanced text.

Error Analysis by Category

Category	TF-IDF Error	BERT Error
Negation	5.1%	20.4%
Mixed Opinion	11.9%	54.9%
Very Long	9.6%	48.9%
Normal	10.5%	51.1%

---

🚀 Quick Start

1. Train the model

cd backend
pip install -r requirements.txt
python train.py