Sentiment Analysis for E-Commerce Reviews

Sentiment Analysis for E-Commerce Reviews: AI-Powered Customer Insights

Challenge:
E-commerce businesses struggle to analyse large volumes of customer reviews manually. Understanding customer sentiment is crucial for improving products, refining marketing strategies, and enhancing user experience. However, traditional methods are time-consuming and inefficient.

Solution:
We developed an NLP-based sentiment analysis model to classify Amazon product reviews as positive, neutral, or negative. By leveraging natural language processing techniques, the model automatically analysed review text, identified sentiment patterns, and provided structured insights. This allowed businesses to gauge customer satisfaction at scale and make data-driven decisions.

Results:
- Enhanced Customer Insights – Enabled businesses to understand customer feedback more effectively.
- Better Product Recommendations – Improved personalisation by aligning suggestions with customer sentiment.
- Optimised Marketing Strategies – Allowed brands to tailor campaigns based on real customer perceptions.

This project demonstrated how AI-driven sentiment analysis can transform e-commerce by automating review analysis, leading to more informed business decisions and improved customer experiences.

🔗 [GitHub Repository](https://github.com/MSenhoury/Sentiment-Analysis)