Welcome to the Amazon Product Review Sentiment Analysis repository! This project leverages Natural Language Processing (NLP) to analyze and classify Amazon product reviews as positive, negative, or neutral. By understanding customer sentiment, businesses and researchers can gain actionable insights to improve product offerings and customer experience. 🌟
🔍 Overview Amazon reviews provide a wealth of unstructured text data that can be challenging to interpret at scale. This project demonstrates how to process and analyze these reviews using advanced NLP techniques, making it easy to classify sentiment and uncover trends.
🚀 Features Data Preprocessing: Tokenization, stop-word removal, stemming/lemmatization, and vectorization (TF-IDF, word embeddings, etc.). Sentiment Classification: Models trained to categorize reviews into sentiment labels. Modeling Frameworks: Support for traditional ML algorithms (Logistic Regression, SVM) and deep learning approaches (LSTMs, Transformers). Performance Metrics: Evaluate models using metrics such as accuracy, precision, recall, and F1-score. Visualization: Sentiment distribution plots, word clouds, and confusion matrices to interpret results. 🛠️ Technologies Used Python 🐍 NLP Libraries: NLTK, SpaCy, Hugging Face Transformers Machine Learning Frameworks: Scikit-learn, TensorFlow, PyTorch Data Visualization: Matplotlib, Seaborn, Plotly Dataset: Publicly available Amazon product reviews dataset