IMDB Sentiment Analysis

This project implements a sentiment analysis model to classify IMDB movie reviews as either positive or negative using TensorFlow and Keras.

Dataset

The dataset used is the IMDB dataset, which contains 50,000 movie reviews labeled as positive or negative. The dataset is divided into a training set, validation set, and test set.

• Training set: 25,000 reviews
• Test set: 25,000 reviews

How to Run

1. Clone the Repository

git clone https://github.com/SNNCS/IMDB_Sentiment_Analysis.git
cd IMDB_Sentiment_Analysis

2. Install Dependencies

Install the required dependencies using pip. If you're using a GPU, you can install the GPU-specific TensorFlow version.

pip install -r requirements.txt

3. Download the Dataset

The dataset will be automatically downloaded when running the code. If you want to manually download it, visit this link and place it in the data/ folder.

4. Train the Model

The model is defined and trained in src/model.py. To train the model, run the following command:

python src/model.py

5. Evaluate the Model

After training, evaluate the model's performance on the test dataset by running:

python src/evaluate.py

6. Results

After training for 10 epochs, the model's loss and accuracy on the test dataset will be printed. Additionally, a plot showing the training and validation loss over epochs will be displayed.

Model Architecture

The model consists of the following layers:

• Embedding layer: Converts word indices into dense vectors of fixed size.
• Dropout layers: Helps prevent overfitting.
• GlobalAveragePooling1D: Averages the word embeddings to form a fixed-size output.
• Dense layer: A fully connected layer for binary classification.

Dependencies

• TensorFlow >= 2.0
• NumPy
• Matplotlib

To install all dependencies:

pip install -r requirements.txt