Sentiment Analysis on Movie Reviews

Project Overview

This project implements a sentiment analysis model using Long Short-Term Memory (LSTM) networks to classify IMDB movie reviews as positive or negative. The model leverages deep learning techniques for text analysis, providing a robust solution to evaluate user sentiments.

Features

• Binary classification of movie reviews (positive or negative).
• Preprocessing pipeline with tokenization and padding for textual data.
• LSTM-based architecture for sequential data learning.
• A user-friendly function for real-time sentiment predictions.

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Technologies Used

• Programming Language: Python
• Libraries:
  • TensorFlow/Keras
  • Pandas
  • Scikit-learn
• Tools: Kaggle API for dataset retrieval

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Dataset

• Source: IMDB Dataset of 50K Movie Reviews
• Access: Downloaded via Kaggle API.
• Structure: Includes 50,000 movie reviews labeled as "positive" or "negative."

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Workflow

1. Dataset Handling

• Download: Use the Kaggle API to fetch the dataset.
• Extraction: Extract the CSV file from the zip archive.

2. Data Preprocessing

• Load the dataset using Pandas.
• Convert sentiment labels into numerical values (positive: 1, negative: 0).

3. Train-Test Split

• Split the data into 80% training and 20% testing subsets.

4. Tokenization and Padding

• Tokenize text to convert words into sequences of integers.
• Apply padding to ensure consistent sequence lengths for the LSTM model.

5. Model Architecture

The LSTM model consists of:

• Embedding Layer: Converts word indices to dense vectors.
• LSTM Layer: Processes sequential data for sentiment classification.
• Dense Output Layer: Sigmoid activation for binary classification.

6. Model Training

• Trained for 5 epochs with a batch size of 64.
• Validation split: 20% of the training data.

7. Model Evaluation

• Evaluate the trained model on the test dataset.
• Metrics: Accuracy and binary cross-entropy loss.

8. Sentiment Prediction

• Implement a function to classify the sentiment of user-provided reviews.

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Results

• The LSTM model achieved significant accuracy on the test data.
• Example Predictions:
  • Input: "This movie was not so interesting." -> Prediction: Negative
  • Input: "This movie was very amazing." -> Prediction: Positive

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How to Run

1. Clone this repository:

   git clone https://github.com/NarendraYSF/LSTMEmotion-Sentiment-Analytics.git

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the Jupyter Notebook to train, evaluate, and test the model.

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Future Scope

• Expand Dataset: Include more diverse reviews to improve generalization.
• Optimize Hyperparameters: Experiment with different learning rates, batch sizes, and epoch counts.
• Alternative Architectures: Explore GRU, Transformer-based models (e.g., BERT).
• Deploy: Build a web or API interface for real-time predictions.

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License

This project is licensed under the GNU General Public License.