README.md

metadata

license: mit
language:
  - en
tags:
  - sentiment-analysis
  - imdb
  - scikit-learn
  - mlp
  - text-classification
pipeline_tag: text-classification

IMDB Sentiment MLP

This model is a course project for IMDB movie-review sentiment classification.

It uses a TF-IDF text representation followed by a small scikit-learn MLP neural network.

Metrics

• Accuracy: 82.00%
• Train samples: 400
• Test samples: 100
• Dataset: imdb_top_500.csv
• Labels: 0 = negative, 1 = positive

Files

• model.joblib: full scikit-learn pipeline
• vectorizer.joblib: standalone TF-IDF vectorizer
• metrics.json: training and evaluation metrics

Example

import joblib

model = joblib.load("model.joblib")
prediction = model.predict(["This movie is great and deeply moving."])[0]
print("positive" if prediction == 1 else "negative")