Skip to content

README.md

Latest commit

 

History

History
77 lines (51 loc) · 1.97 KB

README.md

File metadata and controls

77 lines (51 loc) · 1.97 KB
Raw

🛍️ Amazon Review Sentiment Analyser

Classifies Amazon product reviews as Positive, Neutral, or Negative using two approaches — a simple TF-IDF baseline and a fine-tuned DistilBERT transformer model.

📌 What it does

Takes raw Amazon review text as input and predicts the sentiment:

  • ⭐ 4–5 stars → Positive
  • ⭐ 3 stars → Neutral
  • ⭐ 1–2 stars → Negative

📦 Dataset

Dataset not included in this repo. Download from Kaggle and place the CSV in the root folder.

📊 Model Comparison

Model Accuracy Notes
TF-IDF + Logistic Regression fill yours Fast, lightweight baseline
DistilBERT (pre-trained) fill yours Transformer-based, much more accurate

🗂️ Project Structure

├── sentiment_analysis.ipynb   # Main notebook (EDA + training + evaluation)
├── requirements.txt           # Python dependencies
└── README.md

🚀 How to run it

Option 1 — Google Colab (recommended, free)

  1. Open sentiment_analysis.ipynb in Google Colab
  2. Go to Runtime → Change runtime type → T4 GPU
  3. Download the dataset from Kaggle and upload the CSV to Colab
  4. Run all cells top to bottom (Shift + Enter)

Option 2 — Run locally

git clone https://github.com/YOUR_USERNAME/amazon-sentiment-analyser.git
cd amazon-sentiment-analyser
pip install -r requirements.txt
jupyter notebook sentiment_analysis.ipynb

🛠️ Tech Stack

  • Python 3.10+
  • HuggingFace Transformers (distilbert-base-uncased-finetuned-sst-2-english)
  • Scikit-learn (TF-IDF + Logistic Regression)
  • Pandas, Seaborn, Matplotlib
  • Google Colab (training environment)

📝 License

MIT License — free to use and modify.