Sentiment Analysis on Amazon Product Reviews

Overview

This project applies Natural Language Processing (NLP) techniques to analyze and classify customer sentiments in Amazon product reviews. By predicting whether a review expresses a positive or negative sentiment, the model enables businesses to better understand customer feedback and inform product development and marketing strategies.

Objectives

• Classify Amazon product reviews into positive or negative sentiment.
• Preprocess and clean raw text data for NLP tasks.
• Train and evaluate multiple machine learning models on sentiment-labeled reviews.
• Visualize insights such as sentiment distribution and common keywords.

Dataset

• Source: Amazon product reviews dataset.
• Features:
  • reviewText: Full text of the review.
  • overall: Numerical rating (used to derive sentiment).
  • summary: Short title of the review.
• Target Variable: Sentiment label (positive or negative) based on review score.

Tools & Technologies

• Python
• pandas, NumPy – Data manipulation
• NLTK, spaCy – Text preprocessing
• scikit-learn, Logistic Regression, Naive Bayes, SVM – Modeling
• TF-IDF, CountVectorizer – Feature extraction
• Matplotlib, Seaborn, WordCloud – Visualization
• Jupyter Notebook – Interactive analysis