Improving Sentiment Polarity Detection Through Target Identification

Basiri, Mohammad Ehsan and Abdar, Moloud and Kabiri, Arman and Nemati, Shahla and Zhou, Xujuan and Allahbakhshi, Forough and Yen, Neil Y. (2019) Improving Sentiment Polarity Detection Through Target Identification. IEEE Transactions on Computational Social Systems . ISSN 2329-924X

Abstract

In an opinionated long review, there may be several targets described by different potential terms. Traditional review-level techniques for Persian sentiment analysis addressed the problem using a one-method-fits-all solution in which the overall polarity of a review is calculated using all its opinionated words without considering their target. In this article, a new method is proposed, which first decomposes a long review into its constituent sentences and then detects the main target of each sentence. In the next step, five policies, including most occurring first (MOF), most general first (MGF), most specific first (MSF), first occurring first (FOF), and last occurring first (LOF), are proposed to come up with the main target of the review. Finally, using the part-of-speech (POS) tags, potential terms in the sentences are specified and a comprehensive sentiment lexicon is employed to compute the polarity of the sentences. In order to evaluate the proposed method, three data sets of user reviews about different topics, including digital equipment, hotels, and movies, are created as no previous study addressed the problem of target identification in the Persian language. The results of comparing the proposed method with a state-of-the-art lexicon-based method show that specifying the main targets of reviews can improve the performance of the systems about 17% and 12% in terms of accuracy and F1-measure. Moreover, the proposed method using the MGF policy achieves the best performance in finding the main target of reviews, while for finding the ultimate polarity of reviews, the MOF outperforms other policies.


Statistics for USQ ePrint 37425
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise (1 July 2013 -)
Faculty/School / Institute/Centre: Current - Institute for Resilient Regions - Centre for Health, Informatics and Economic Research (1 Aug 2018 -)
Date Deposited: 28 Jan 2020 01:35
Last Modified: 07 Feb 2020 00:22
Uncontrolled Keywords: Index Terms— Lexicon-based approach; opinion mining; Persian language; sentiment analysis (SA); text mining
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1109/TCSS.2019.2951326
URI: http://eprints.usq.edu.au/id/eprint/37425

Actions (login required)

View Item Archive Repository Staff Only