Explicit aspects extraction in sentiment analysis using optimal rules combination

Tubishat, Mohammad and Idris, Norisma and Abushariah, Mohammad (2021) Explicit aspects extraction in sentiment analysis using optimal rules combination. Future Generation Computer Systems, 114. pp. 448-480. ISSN 0167-739X, DOI https://doi.org/10.1016/j.future.2020.08.019.

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Aspect extraction represents a core task of aspect-based sentiment analysis. This study presents a supervised aspect extraction algorithm for explicit aspect extraction from formal and informal texts. To accomplish the new algorithm, 126 aspect extraction rules are combined to cover both formal and informal texts, because customer reviews are a mix of formal and informal texts. These 126 rules include certain dependency-based rules and pattern-based rules from previous studies, in addition to newly developed rules intended to overcome prior rules' weaknesses. In addition, many aspect extraction rules have remained unexplored by previous studies. However, many of these 126 rules are irrelevant and should be removed. Thus, a prober selection of the included rules is required. Therefore, in this study we also improved the Whale Optimization Algorithm (WOA) to address rules selection problem with an improved algorithm called improved WOA (IWOA). Two major improvements were included into IWOA. The first improvement is the development of a new update equation based on Cauchy mutation to improve WOA population diversity. The second improvement is the development of a new local search algorithm (LSA) to solve WOA local optima. The IWOA algorithm is applied on the full set of rules to select best rules subset and remove low quality rules. Finally, a new pruning algorithm (PA) has been developed to remove incorrect aspects and retain correct aspects. The Results on seven benchmark datasets demonstrate that IWOA+PA outperforms all other state-of-the-art baseline works and most recent works. (C) 2020 Elsevier B.V. All rights reserved.

Item Type: Article
Funders: University of Malaya, Malaysia Grant - Faculty Program [GPF007D-2018]
Uncontrolled Keywords: Sentiment analysis; Dependency-based rules; Pattern-based rules; Improved whale optimization algorithm; Explicit aspect extraction
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology > Department of Artificial Intelligence
Depositing User: Ms Zaharah Ramly
Date Deposited: 13 Apr 2022 07:28
Last Modified: 13 Apr 2022 07:28
URI: http://eprints.um.edu.my/id/eprint/27043

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