Improving Sentiment Polarity Detection Through Target Identification
Article
Article Title | Improving Sentiment Polarity Detection Through Target Identification |
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ERA Journal ID | 212762 |
Article Category | Article |
Authors | Basiri, Mohammad Ehsan (Author), Abdar, Moloud (Author), Kabiri, Arman (Author), Nemati, Shahla (Author), Zhou, Xujuan (Author), Allahbakhshi, Forough (Author) and Yen, Neil Y. (Author) |
Journal Title | IEEE Transactions on Computational Social Systems |
Journal Citation | 7 (1), pp. 113-128 |
Year | 2020 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 2329-924X |
Digital Object Identifier (DOI) | https://doi.org/10.1109/TCSS.2019.2951326 |
Web Address (URL) | https://ieeexplore.ieee.org/document/8911218/authors#authors |
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. |
Keywords | Index Terms— Lexicon-based approach; opinion mining; Persian language; sentiment analysis (SA); text mining |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | © 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. |
Byline Affiliations | Shahrekord University, Iran |
University of Montreal, Canada | |
University of New Brunswick, Canada | |
School of Management and Enterprise | |
Islamic Azad University, Iran | |
University of Aizu, Japan | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5758/improving-sentiment-polarity-detection-through-target-identification
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