Enhancing purchase decision using multi-word target bootstrapping with part-of-speech pattern recognition algorithm

摘要

In this research work, multi-word target related terms are extracted automatically from the customer reviews for sentiment analysis. We used LIDF measure and have proposed a novel measure called, TC_(umass)in iterative multi-word target (IMWT) bootstrapping algorithm. In addition, part-of-speech pattern recognition (PPR) algorithm has been proposed to identify the appropriate target and emotional words from multi-word target related terms. This article aims to bring out both implicit and explicit targets with their corresponding polarities in an unsupervised manner. We proposed two models namely, MWTB without PPR and MWTB with PPR. Thus, the present research illustrates the comparison between the proposed works and the existing multi-aspect bootstrapping (MAB) algorithm. The experiment has been done based on different data sets and thereafter the performance evaluated using different measures. From this study, the result expounds that MWTB with PPR model performs well, having achieved the precise targets and emotional words.