Transformer-Sentiment-Model-Movie-Reviews

Instructions:

Welcome to the "Transformer-Sentiment-Model-Movie-Reviews" notebook! This notebook provides a step-by-step guide to building and evaluating a sentiment analysis model on movie reviews using Transformer architecture, particularly the BERT model.

Key Highlights of the Process:

Explore Data:

Unravel the sentiment distribution of movie reviews and gain insights through distribution plots and word clouds. Uncover the frequency and distribution of sentiments, laying the groundwork for further analysis.

Preprocess Data:

Embark on a journey of data preprocessing, including handling NaN values, removing stop words, and tokenization. Witness the transformation of raw text data into a format suitable for model training.

Build and Train Our Classification Model:

Engage with the powerful BERT (Bidirectional Encoder Representations from Transformers) model for sequence classification. Customize the pre-trained BERT model for sentiment analysis tasks and fine-tune it on our movie review dataset. Follow the training loop meticulously crafted for model optimization and evaluation.

Optimizer & Learning Rate Scheduler:

Configure hyperparameters such as batch size, learning rate, and epochs for optimal training performance. Navigate through the training loop, tracking progress and monitoring performance metrics.

Test the Loaded Model:

Evaluate the trained model's performance on test data, witnessing its prowess in predicting sentiment labels for movie reviews. Utilize a helper function to streamline the prediction process and visualize sentiment predictions in action.

With meticulous attention to detail and a deep dive into the intricacies of sentiment analysis, this Kaggle notebook serves as a beacon for enthusiasts seeking to master the art of sentiment analysis on movie reviews.

Testing the Model:

To ensure the robustness and effectiveness of our sentiment analysis model, we perform rigorous testing on unseen data. Below, we showcase the function wrap_pred_output() designed to streamline the prediction process and provide clear sentiment predictions based on textual input.

def wrap_pred_output(score, text):
    sentiment_text = get_sentiment_pred(score)
    print(f"Given text: '{text}'\nSeems to be {sentiment_text}\n")
 
# Test the model predictions
wrap_pred_output(**pred("This movie is awesome"))
wrap_pred_output(**pred("This movie is terrible"))

Results:

Given text: 'This movie is awesome'
Seems to be positive

Given text: 'This movie is terrible'
Seems to be negative

These results showcase the model's capability to accurately predict sentiment labels for movie reviews. By providing clear and concise sentiment interpretations, our sentiment analysis model proves its efficacy in understanding and categorizing textual input.