Identifying opinion mining elements based on dependency relations and fuzzy logic

Abstract

Opinion mining mainly involves three elements: feature and feature-of relations, opinion expressions and the related opinion attributes (e.g. Polarity), and feature-opinion relations. Although many works are emerged to achieve the aim of gaining information, the previous researches typically handle each of the three elements in isolation that cannot give the sufficient information extraction results and hence increases the complexity and the running time of information extraction. In this paper, we propose an opinion mining extraction algorithm to jointly discover the main opinion mining elements. Specifically, the algorithm automatically builds kernels to combine closest words into new terms from word level to phrase level based on dependency relations, and we ensure the accuracy of opinion expressions and polarity based on fuzzy measurements, opinion degree intensifiers, and opinion patterns. The analyzed reviews show that the proposed algorithm can effectively identify the main elements simultaneously and outperform the baseline methods. - 2019 ICAI 2015 - WORLDCOMP 2015. All rights reserved.