Abstract
It is challenging to figure out the most common appraisal of an online product since there are too many reviews about it uploaded on the internet. Several research methods using opinion mining in the context of the online reviews have been suggested to solve this issue. The existing research on opinion mining can be classified into three general levels: document-level, sentence-level, and aspect-level sentiment analysis. Aspect-based evaluation is the most meaningful application in opinion mining, and researchers are getting more interested in product aspect extraction; however, more complex algorithms are needed to address this issue precisely with larger corpora. This paper introduces a method to automatically gain a knowledge-based system, which then is used to capture product aspects and corresponding opinions from a large number of product reviews in a specific domain. Our efforts tend to improve accuracy and the usefulness of review summaries by leveraging knowledge of product aspect extraction and provide both appropriate level of detail and richer representation capabilities.
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