Mechanism for Crawling, Filtering, and Presenting Opinionated Content on Online Products to the Customers

Mechanism for Crawling, Filtering, and Presenting Opinionated Content on Online Products to the Customers

Rosy Madaan
Copyright: © 2023 |Pages: 14
DOI: 10.4018/978-1-6684-5255-4.ch010
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Abstract

There is a large amount of data available on the web in form of opinions, which need to be accessed for mining opinions. This is an ever-growing field that brings together the reviews, blogs, discussions on forums, Twitter, microblogs, and social networks. A user may be looking for opinions on some commodity or product for making decision regarding purchase for which there is the need of a system based on question answering. This gives rise to a question answering (QA) system. This system works on all the aspects of question answering along with the mining of opinions. The chapter discusses all the modules of the question answering system along with how the opinions are mined. The details of implementation along with the performance analysis of the proposed system are given in the chapter. On performance evaluation, a high value of opinion accuracy has been found that shows that the system performs well.
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Liu (2008) focuses on methods in the direction of addressing the new challenges that arise by sentiment aware applications, as compared to those that are already present in more traditional fact-based analysis (Sadegh, Ibrahim, and Othman, 2012). The paper presented the work done on how evaluative text can be summarized. Also, it discussed issues in broader terms regarding privacy, economic impact and manipulation that arise due to the development of information-access services which are opinion-oriented.

O’Leary (2015) presented that review summarization, review classification, extraction of synonyms and antonyms, tracking of opinions in online discussions etc. are some applications of sentiment classification in information systems. Yu, Zha, and Chua (2012) worked on the sentiment classification problem at different levels i.e. at word-level, document-level, sentence-level and aspect-level. Also, some techniques for solution of these problems have been introduced.

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