Opinion Mining-Based Term Extraction Sentiment Classification Modeling

Abstract

The spread of social media has accelerated the formation and dissemination of user review data, which contain subjective opinions of users on products, in an e-commerce environment. Because these reviews significantly influence other users, opinion mining has garnered substantial attention in analyzing the positive and negative opinions of users and deriving solutions based on these analytical results. Terms that include sentimental information and used in user reviews serve as the most crucial element in sentimental classification. In this regard, it is crucial to distinguish the most influential terms in user reviews. This study proposed a document-level sentiment classification model based on the collection and application of user reviews generated in an e-commerce environment. Here, a term information extraction method was applied to the proposed model to select core terms, classify the selected terms according to parts of speech (POS), determine terms that can increase information power and influence, and adopt these terms in opinion mining research, based on SVM, SVM+, and SVM+MTL techniques. The results obtained from evaluating the proposed model indicate that it exhibited excellent sentiment analysis performance. The proposed model is expected to be effectively utilized in providing enhanced services for users and increasing competitiveness in the e-commerce environment.