Amazon Reviews Sentiment Analysis with BERT

This project demonstrates a sentiment analysis on Amazon reviews using the BERT model. It uses bert-tensorflow for text processing and TensorFlow Hub to fine-tune BERT embeddings for classification tasks. This notebook is designed to evaluate how well BERT can classify reviews as positive or negative based on the provided Amazon dataset.

Project Structure

• Data Preprocessing: Includes text cleaning and tokenization using BERT's FullTokenizer.
• Model Building: Constructs a BERT-based classification model using tensorflow_hub.
• Training and Evaluation: Splits the dataset, trains the model, and evaluates its performance on the test set.
• Visualization: Provides insights into model performance through visualizations.

Setup

To run this notebook, install the required packages:

pip install bert-tensorflow tqdm tensorflow_hub

Usage

• Clone this repository and navigate to the project directory.
• Run the notebook using Jupyter or Google Colab

Results

This project aims to analyze the effectiveness of BERT in sentiment classification on real-world data from Amazon reviews.