CONSTRUCTING THAI OPINION MINING
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Published:
Apr 26, 2018
Keywords:
Opinion Mining Sentiment Analysis Implicit Opinion
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Abstract
Nowadays the growth of Internet technology makes people share opinions in social media, forum and webboard easily. This causes the huge volume of opinionated text. Opinion mining aims for analyzing opinion sentences for various purposes such as to help consumers in decision makings and to allow entrepreneurs to know the reputations of their products. This article reviews opinion mining concepts and principle that focuses on sentiment analysis in implicit opinions for Thai language to summarize the polarities of opinions that express positive or negative. The analysis and synthesis of related research has showed that feature-based sentiment analysis provides finer grains than the other levels, and the sentiment analysis in explicit opinion for Thai language still has a limitation in that polar words have to be expressed explicitly in sentences. This leads to the suggestion of sentiment analysis in implicit opinions for Thai language by using words or phrases extraction in feature level together with the determination of surrounding context.
Article Details
Section
บทความวิชาการ
References
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ตรีเทพ ธำรงลักษณ์ และ จันทร์เจ้า มงคลนาวิน. 2554. “ การพัฒนาระบบสรุปบทวิจารณ์สินค้าภาษาไทยโดยผู้บริโภคแบบอัตโนมัติ.” จุฬาลงกรณ์ธุรกิจปริทัศน์ ปีที่ 33, ฉบับที่ 128.
บุญเสริม กิจศิริกุล. 2546. อัลกอริทึมการทำเหมืองข้อมูล. กรุงเทพฯ: ภาควิชาวิศวกรรมคอมพิวเตอร์ จุฬาลงกรณ์มหาวิทยาลัย.
วรัญญา วรรณศรี. 2553. “ระบบวิเคราะห์ข้อความแสดงความคิดเห็นสำหรับโรงแรม.” สืบค้นเมื่อวันที่ 19 พฤศจิกายน 2553.
Alisa Kongthon, Niran Angkawattanawit, Chatchawal Sangkeettrakarn, Pornpimon Palingoon and Choochart Haruechaiyasak. 2010. “Using an Opinion Mining Approach to Exploit Web Content in Order to Improve Customer Relationship Management.” Proceedings of Technology Management for Global Economic Growth. Retrieved August 10, 2014 from IEEE Xplore Digital Library.
Alisa Kongthon, et al. 2011.“HotelOpinion: An Opinion Mining System on Hotel Reviews in Thailand.” Technology Management in the Energy Smart
World (PICMET), 2011 Proceedings of PICMET '11. July 31 2011- 4 August 2011.
Bing Liu. 2012. “Sentiment Analysis and Opinion Mining.” Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers.
Choochart Haruechaiyasak, et al. 2010. “Constructing Thai Opinion Mining Resource: A Case Study on Hotel Reviews.” Proc. of the Eighth Workshop on Asian Language Resources: 64–71.
Hu, M. , and Liu, B. 2004. “Mining Opinion Features in Customer Reviews.” In Proceedings of Nineteenth National Conference on Artificial Intelligence.
Hu, M., and Liu, B. 2004. “Mining and summarizing customer reviews.” In KDD : Proc. of the tenth ACM SIGKDD Conference on Knowledge discovery and data mining: 168-177.
Kim, S. and E. Hovy. 2004. “Determining the Sentiment of Opinions.” In Proc. of the Intl. Conf. on Computational Linguistics (COLING’04):pp.1367-1373.
Kritsada Sriphaew, Takamura, Hiroya, and Okumura, Manabu. 2009. “Sentiment Analysis for Thai Natural Language Processing.” Proceedings of the 2nd Thailand-Japan International Academic Conference, TJIA 2009. Kyoto, Japan: 123-124.
Max Bramer. 2007. Principles of Data Mining. Springer. pp.173 - 176.
Oelke, D., et al. 2009. “Visual Opinion Analysis of Customer Feedback Data.” IEEE Symposium on Visual Analytics Science and Technology, Atlantic City, New Jersey, USA.
Pang, B., L. Lee, and S. Vaithyanathan. 2002. “Thumbs up? Sentiment Classification Using Machine Learning Techniques.” In Proc. Ofthe ACL-02 Conference on Empirical Methods in Natural Language Processing: pp. 79-86.
Popescu, A., and Etzioni, O. 2005. “Extracting Product Features and Opinions from Reviews.” Proceeding HLT '05 Proceedings of the conference on Human
Language Technology and Empirical Methods in Natural Language Processing: 339-346.
Qiu, Guang., et al. 2009. “Expanding Domain Sentiment Lexicon through Double Propagation.” In Proceedings of IJCAI 2009.
Riloff, Ellen, Wiebe, Janyce, and Wilson, Theresa. 2003. “Learning subjective nouns using extraction pattern bootstrapping.” Proceedings of CoNLL 2003.
Turney, P. 2002. “Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews.” In Proc. of the Meeting of the Association for Computational Linguistics (ACL’02): pp.417–424.
Zhail, Yongyong, et al. 2010. “Extracting Opinion Features in Sentiment Patterns.” International Conference on Information, Networking and Automation
(ICINA).Zhang, Lei, and Liu, Bing. 2010. “Extracting and Ranking Product Features in Opinion Documents.” Proceedings of the 23rd International Conference on Computational. Linguistics (COLING-2010). August 23-27.
Beijing, China.Zhang, Lei, and Liu, Bing. 2011. “Identifying Noun Product Features that Imply Opinions.” Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:shortpapers. Portland, Oregon.June 19-24, 2011: 575-580.
Zhang, Lei, and Liu, Bing. 2011. “Extracting Resource Terms for Sentiment Analysis.” Proceedings of the 5th International Joint Conference on Natural Language Processing (IJCNLP-2011). November 8-13, 2011.Chiang Mai,Thailand.