Identifying Explicit Features of Persian Comments

Document Type : Research Article

Authors

1 Department of Computer Engineering, Yazd University, Yazd, Iran.

2 Department of Computer Engineering, University of Isfahan, Isfahan, Iran.

Abstract

Recently, the approach towards mining various opinions on weblogs, forums and websites has gained attentions and interests of numerous researchers. In this regard, feature-based opinion mining has been extensively studied in English documents in order to identify implicit and explicit product features and relevant opinions. However, in case of texts written in Persian language, this task faces serious challenges. The objective of this research is to present an unsupervised method for feature-based opinion mining in Persian; an approach which does not require a labeled training dataset. The proposed method in this paper involves extracting explicit product features. Previous studies dealing with extraction of explicit features often focus on lexical roles of words; the approach which cannot be used in distinguishing between an adjective as a part of a noun or a sentiment word. In this study, in addition to lexical roles, syntactic roles are also considered to extract more relevant explicit features. The results demonstrate that the proposed method has got higher recall and precision values compared to prior studies.

Keywords


[1] E. Lloret, A. Balahur, J. M. Gómez, A. Montoyo, and M. Palomar. Towards a unified framework for opinion retrieval, mining and summarization. Journal of Intelligent Information Systems, 39(3):711–747, 2012. [ bib | www: ]
[2] A. Bagheria, M. Saraeeb, and F. de Jong. Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews. Knowledge-Based System, 52:201--213, 2013. [ bib | www: ]
[3] Bing Liu. Sentiment analysis and subjectivity. In Handbook of natural language processing, pages 627--666, 2010. [ bib | www: ]
[4] Bing Liu. Sentiment analysis and opinion mining. In Synthesis Lectures on Human Language Technologies, pages 1--167. Morgan & Claypool Publishers, 2012. [ bib | www: ]
[5] G. Vinodhini and RM. Chandrasekaran. Sentiment analysis and opinion mining: a survey. International Journal, 2(6):282--292, 2012. [ bib ]
[6] S. Moghaddam and M. Ester. Opinion digger: an unsupervised opinion miner from unstructured product reviews. In Proceedings of the 19th ACM international conference on Information and knowledge management, pages 1825--1828. ACM, 2010. [ bib | www: ]
[7] Zhen Hai, Kuiyu Chang, and G. Cong. One seed to find them all: mining opinion features via association. In Proceedings of the 21st ACM international conference on Information and knowledge management, pages 255--264. ACM, 2012. [ bib | www: ]
[8] H. Xu, F. Zhang, and W. Wang. Implicit feature identification in Chinese reviews using explicit topic mining model. Knowledge-Based Systems, 76:166--175, 2015. [ bib | www: ]
[9] M. Hu and B. Liu. Mining opinion features in customer reviews. In Proceedings of the 19th national conference on Artifical intelligence, pages 755--760, 2004. [ bib ]
[10] G. Qiu, B. Liu, J. Bu, and C. Chen. Expanding Domain Sentiment Lexicon through Double Propagation. In Proceedings of the 21st international jont conference on Artifical intelligence, pages 1199--1204. [ bib | www: ]
[11] L. Zhang, B. Liu, S. Hwan Lim, and E. O'Brien-Strain. Extracting and ranking product features in opinion documents. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pages 1462--1470, 2010. [ bib ]
[12] G. Qiu, B. Liu, J. Bu, and C. Chen. Opinion word expansion and target extraction through double propagation. Computational linguistics, 37(1):9--27, 2011. [ bib | www: ]
[13] Z. Hai, K. Chang, and J. Kim. Implicit feature identification via co-occurrence association rule mining. In International Conference on Intelligent Text Processing and Computational Linguistics, pages 393--404. Springer, Heidelberg, 2011. [ bib | www: ]
[14] W. Wang, H. Xu, and W. Wan. Implicit feature identification via hybrid association rule mining. Expert Systems with Applications, 40(9):3518--3531, 2013. [ bib | www: ]
[15] S. Poria, E. Cambria, L. Ku, C. Gui, and A. Gelbukh. A rule-based approach to aspect extraction from product reviews. In Proceedings of the second workshop on natural language processing for social media (SocialNLP), pages 28--37, 2014. [ bib | www: ]
[16] K. Schouten and F. Frasincar. Implicit Feature Extraction for Sentiment Analysis in Consumer Reviews. In International Conference on Applications of Natural Language to Data Bases/Information Systems, pages 228--231. Springer, 2014. [ bib | www: ]
[17] K. Schouten and F. Frasincar. Extracting Product Features in Persian. In Proceedings of the 3rd Computational Linguistics Conference, Sharif University, 2014. [ bib ]
[18] Ferdowsi University of Mashhad. Natural Language Processing Tools. http://wtlab.um.ac.ir, Web Technology Lab, 2012. [ bib ]
[19] Khalash M Imani M. Persian Language Processing Tool. http://www.sobhe.ir/hazm, 2013. [ bib ]
[20] Ted Dunning. Accurate methods for the statistics of surprise and coincidence. Computational linguistics, 19(1):61--74, 1993. [ bib ]
[21] C. Wei, Y. Chen, C. Yang, and C. C. Yang. Understanding what concerns consumers: a semantic approach to product feature extraction from consumer reviews. Information Systems and e-Business Management, 8(2):149–167, 2010. [ bib | www: ]
[22] uplooder. Digikala Dataset (2018, December 13). https://www.uplooder.net/files/a3cbdeb80f2b59d51cd858ab0f4d4558/DataSet.rar.html, Retrieved February 2, 2019. [ bib ]
Volume 6, Issue 1
January 2019
Pages 1-11
  • Receive Date: 12 August 2018
  • Revise Date: 17 September 2019
  • Accept Date: 29 September 2019
  • First Publish Date: 29 September 2019