IMDb Reviews Sentiment & Emotion Analysis

📌 Project Overview

This project focuses on sentiment and emotion analysis of IMDb movie reviews. The goal is to extract insights from user-generated content by applying advanced natural language processing (NLP) techniques, improving traditional sentiment classification with emotion detection and real-time analysis.

📊 Dataset

• Source: IMDb movie reviews dataset
• Contains user reviews with corresponding sentiment labels (positive/negative)
• Extended with emotion detection (e.g., joy, anger, sadness, surprise, etc.)
• Preprocessed for stopwords removal, tokenization, and vectorization

🛠️ Technologies Used

• Python (pandas, numpy, matplotlib, seaborn)
• NLP & Machine Learning (NLTK, Scikit-learn, TensorFlow, Transformers)
• Vectorization (TF-IDF, Word2Vec, BERT embeddings)
• Deep Learning (LSTMs, Transformers for emotion detection)
• Deployment (Flask/FastAPI for API, Streamlit for visualization)

📈 Methodology

Step 1: Import Libraries

• Import pandas for data manipulation
• Import NLTK for natural language processing

Step 2: Load Dataset

• Mount the CSV file to VSCode
• Read the dataset using pandas

Step 3: Data Preprocessing

• Removing HTML tags
• Importing NLTK for text processing
• Removing stop words
• Text lemmatization
• Removing noise
• Adding a new column for cleaned text
• Splitting data into training and testing sets

Step 4: Feature Extraction

• Use TF-IDF Vectorizer to transform text into numerical features

Step 5: Model Building and Evaluation

• Train and evaluate machine learning models:
  • Random Forest Classifier
  • Multinomial Naive Bayes
• Test the model on new reviews

🚀 Setup & Installation

# Clone the repository
git clone https://github.com/yourusername/imdb-sentiment-analysis.git
cd imdb-sentiment-analysis

# Install dependencies
pip install -r requirements.txt

📌 Usage

Training Models

python train.py --model sentiment
python train.py --model emotion

Running Sentiment Analysis on New Reviews

python predict.py --text "This movie was absolutely fantastic!"

📅 Future Enhancements

• Implement real-time IMDb review analysis
• Improve accuracy with advanced deep learning techniques
• Add multilingual sentiment & emotion support
• Integrate interactive dashboard for visualization

📝 License

This project is licensed under the MIT License. You can find the full license text in the LICENSE file of this repository.

🤝 Contributing

Pull requests are welcome! For major changes, please open an issue first to discuss your ideas.

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