A Framework of Customer Review Analysis Using the Aspect-Based Opinion Mining Approach

TL;DR

This paper presents a transfer learning-based framework for aspect-based opinion mining from customer reviews, focusing on extracting detailed sentiments about different aspects of products.

Contribution

It introduces a novel transfer learning approach for aspect and entity extraction in opinion mining, applied to real-world Amazon reviews.

Findings

Achieved satisfactory results in aspect-based opinion mining tasks.

Demonstrated effectiveness of transfer learning in extracting detailed opinions.

Improved accuracy over traditional methods in aspect and entity extraction.

Abstract

Opinion mining is the branch of computation that deals with opinions, appraisals, attitudes, and emotions of people and their different aspects. This field has attracted substantial research interest in recent years. Aspect-level (called aspect-based opinion mining) is often desired in practical applications as it provides detailed opinions or sentiments about different aspects of entities and entities themselves, which are usually required for action. Aspect extraction and entity extraction are thus two core tasks of aspect-based opinion mining. his paper has presented a framework of aspect-based opinion mining based on the concept of transfer learning. on real-world customer reviews available on the Amazon website. The model has yielded quite satisfactory results in its task of aspect-based opinion mining.