A Novel approach for Aspect Based Opinion Mining using Enhanced Grey Wolf Optimization - Shilpi Gupta (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The online reviews based on aspects shared by the users have become a common source of customer knowledge on purchasing choices and businesses pursuing opinions. In this work, a new aspect-based opinion mining technique with four major modules: Pre-processing, Aspect sentiment extraction, Aspect grouping and aspect-sentiment classification (ASC) is introduced. Initially, the collected reviews from users are pre-processed via the following steps: Tokenization, Lemmatization, Stemming and Stop word removal. Subsequently, the aspect sentiment extraction is accomplished by following two major phases: POS tagging and Rule based lexicon extraction. The extracted aspects are grouped into primary and second aspect based on the computed semantic similarity score. The mining becomes more precise by giving the dual weightage to the aspects in the review. Accordingly, the weight function is multiplied to both the primary and second aspect. Further, the proposed model tunes the weighting factors of primary and secondary aspects to make the classification more appropriate with respect to opinion. For this, a new Enhanced Grey Wolf with Mutation Operation (EGWMO) model is proposed in this work. Subsequently, the weight optimized primary aspect; weight optimized secondary aspect as well as extracted opinion is considered as the extracted features for final classification via Neural Network. From NN, the sentiments of the reviews are classified as: positive sentiment, extreme, neutral sentiment, extreme positive sentiment or extreme negative sentiment.