Opinion–Aspect Relations in Cognizing Customer Feelings via Reviews

Determining a consensus opinion on a product sold online is no longer easy, because assessments have become more and more numerous on the Internet. To address this problem, researchers have used various approaches, such as looking for feelings expressed in the documents and exploring the appearance and syntax of reviews. Aspect-based evaluation is the most important aspect of opinion mining, and researchers are becoming more interested in product aspect extraction; however, more complex algorithms are needed to address this issue precisely with large data sets. This paper introduces a method to extract and summarize product aspects and corresponding opinions from a large number of product reviews in a specific domain. We maximize the accuracy and usefulness of the review summaries by leveraging knowledge about product aspect extraction and providing both an appropriate level of detail and rich representation capabilities. The results show that the proposed system achieves F1-scores of 0.714 for camera reviews and 0.774 for laptop reviews.