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A Model for Cross-Domain Opinion Target Extraction in Sentiment Analysis

Muhammet Yasin PAK*, Serkan GUNAL

Department of Computer Engineering, Eskisehir Technical University, Eskisehir, Turkiye

* Corresponding Author: Muhammet Yasin PAK. Email: email

Computer Systems Science and Engineering 2022, 42(3), 1215-1239. https://doi.org/10.32604/csse.2022.023051

Abstract

Opinion target extraction is one of the core tasks in sentiment analysis on text data. In recent years, dependency parser–based approaches have been commonly studied for opinion target extraction. However, dependency parsers are limited by language and grammatical constraints. Therefore, in this work, a sequential pattern-based rule mining model, which does not have such constraints, is proposed for cross-domain opinion target extraction from product reviews in unknown domains. Thus, knowing the domain of reviews while extracting opinion targets becomes no longer a requirement. The proposed model also reveals the difference between the concepts of opinion target and aspect, which are commonly confused in the literature. The model consists of two stages. In the first stage, the aspects of reviews are extracted from the target domain using the rules automatically generated from source domains. The aspects are also transferred from the source domains to a target domain. Moreover, aspect pruning is applied to further improve the performance of aspect extraction. In the second stage, the opinion target is extracted among the aspects extracted at the former stage using the rules automatically generated for opinion target extraction. The proposed model was evaluated on several benchmark datasets in different domains and compared against the literature. The experimental results revealed that the opinion targets of the reviews in unknown domains can be extracted with higher accuracy than those of the previous works.

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Cite This Article

M. Yasin PAK and S. GUNAL, "A model for cross-domain opinion target extraction in sentiment analysis," Computer Systems Science and Engineering, vol. 42, no.3, pp. 1215–1239, 2022.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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