📚 IMDB Movie Reviews Text Classification

📝 Table of Contents

• 📚 Overview
• 🔍 Project Details
  • Objective
  • Dataset
  • Methodology
• ✨ Key Features
• ⚙️ Requirements
• 🚀 Getting Started
• 📈 Results and Insights
• 📄 License
• 📧 Contact

📚 Overview

Welcome to the IMDB Movie Reviews Text Classification project! This repository offers an efficient and streamlined approach for classifying the sentiment of IMDB movie reviews, focusing on resource-friendly methods. Ideal for students, data enthusiasts, and professionals, this project highlights best practices for text classification in NLP.

🔍 Project Details

Objective

To classify IMDB movie reviews as positive or negative using models designed for effective and efficient text classification in computationally constrained environments.

Dataset

• Source: IMDB Movie Reviews
• Description: A labeled dataset with text-based movie reviews for binary sentiment classification.
• Access: IMDB Dataset on Kaggle

Methodology

1. Data Preprocessing

   • Cleaning: Removing unnecessary characters, HTML tags, and stop words.
   • Tokenization: Breaking text into meaningful tokens for analysis.
   • Feature Extraction: Applying techniques like TF-IDF to convert text into numerical features.

2. Model Selection

   • Logistic Regression: Effective for binary classification.
   • Naive Bayes: Lightweight and suitable for text data, providing a balance between efficiency and accuracy.

3. Evaluation Metrics

   • Accuracy: Measures prediction correctness.
   • Precision & Recall: Assess the quality of positive predictions and ability to find relevant instances.
   • F1-Score: A single performance metric that combines precision and recall.

✨ Key Features

• Resource Efficiency: Models and techniques are optimized for limited computational power.
• Scalability: Methods can be easily scaled for larger datasets or more complex environments.
• Educational Value: Detailed explanations and clear steps make this project ideal for learning NLP and text classification fundamentals.
• Reproducibility: Easy-to-follow instructions and thorough documentation.

⚙️ Requirements

• Python: Version 3.8 or higher
• Libraries:
  • scikit-learn
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • nltk

All dependencies are listed in requirements.txt.

📈 Results and Insights

The project provides an in-depth evaluation of each model, with metrics like accuracy, precision, recall, and F1-score. Insights into the performance of different approaches within limited-resource constraints help users understand the efficiency vs. accuracy trade-offs.

📄 License

This project is licensed under the MIT License.

📧 Contact

If you have questions or feedback, feel free to reach out:

• Email: fatimaliyva@gmail.com
• LinkedIn: Fatima Aliyeva
• GitHub: FatimaAliyeva01

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