https://github.com/shibin08/sentiment-analysis-movie-reviews

A sentiment analysis project on IMDb movie reviews using Natural Language Processing (NLP) techniques. Text data is cleaned, vectorized using TF-IDF, and classified using machine learning models like Logistic Regression and Random Forest. Achieved high accuracy in distinguishing positive and negative reviews.
https://github.com/shibin08/sentiment-analysis-movie-reviews

logistic-regression machine-learning movie-reviews natural-language-processing random-forest scikit-learn sentiment-analysis text-classification tf-idf

Last synced: 4 months ago
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A sentiment analysis project on IMDb movie reviews using Natural Language Processing (NLP) techniques. Text data is cleaned, vectorized using TF-IDF, and classified using machine learning models like Logistic Regression and Random Forest. Achieved high accuracy in distinguishing positive and negative reviews.

• Host: GitHub
• URL: https://github.com/shibin08/sentiment-analysis-movie-reviews
• Owner: Shibin08
• License: mit
• Created: 2025-06-25T16:50:04.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2025-06-27T19:05:05.000Z (4 months ago)
• Last Synced: 2025-06-27T19:45:33.813Z (4 months ago)
• Topics: logistic-regression, machine-learning, movie-reviews, natural-language-processing, random-forest, scikit-learn, sentiment-analysis, text-classification, tf-idf
• Language: Jupyter Notebook
• Homepage:
• Size: 28.7 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Sentiment-Analysis-Movie-Reviews

This is a machine learning model aimed at analyzing the sentiment of IMDb movie reviews. The objective is to classify reviews as **positive** or **negative** using **TF-IDF vectorization** and **machine learning models** like Logistic Regression and Random Forest.

# Objective

To build a text classification model that identifies sentiment from movie reviews using classical machine learning techniques.

# Dataset

https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews

# Tools and Libraries

- Scikit-learn

- Python

- SpaCy

- Jupyter Notebook

- Pandas, NumPy

- TF-IDF Vectorizer

- Matplotlib / Seaborn

# Results

- **Logistic Regression Accuracy:** ~87%

- **Random Forest Accuracy:** ~84%

- **SVC Accuracy:** ~85%

- Evaluation done using: Accuracy Score, Confusion Matrix, and F1-Score

# Team members

**Group No. 32**

- Santwana Behara(Team Leader)

- Mohammad Rakshanda

- Majji Vivek

- Shibin Malakot