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Sentiment Analysis of Amazon Reviews: Identifying Positive and Negative Feedback

  • Unique Paper ID: 168450
  • Volume: 11
  • Issue: 5
  • PageNo: 1602-1610
  • Abstract:
  • With the rapid increase in user-generated reviews on e-commerce platforms like Amazon, it has become increasingly difficult for both users and businesses to extract meaningful insights from the overwhelming volume of feedback. Manually reading through thousands of reviews for a product can be time-consuming and inefficient, which presents a need for automated systems that can summarize the content effectively. This paper introduces an abstractive summarization model designed to tackle this challenge by automatically condensing lengthy Amazon product reviews into concise, coherent summaries. Unlike extractive summarization, which simply picks key sentences from the original reviews, abstractive summarization generates entirely new sentences that capture the core ideas and overall sentiment of the reviews, leading to more natural, readable summaries. The model employs advanced natural language processing (NLP) techniques to understand the context, structure, and meaning of the reviews, allowing it to produce summaries that are both concise and highly informative. These summaries provide a snapshot of the most relevant points of feedback, helping users make quicker, more informed purchasing decisions without having to sift through extensive reviews. By focusing on these metrics, the paper demonstrates how this summarization model improves the user experience on e-commerce platforms, allowing users to access easily digestible summaries. This innovation not only benefits customers but also helps businesses analyze consumer feedback more efficiently, leading to better product development and marketing strategies.
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  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 5
  • PageNo: 1602-1610

Sentiment Analysis of Amazon Reviews: Identifying Positive and Negative Feedback

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