An Effective Approach to Automatic Feature Based Opinion Lexicon Expansion
DOI:
https://doi.org/10.53555/cse.v3i6.157Keywords:
Opinion Extraction,, Opinion Lexicon Expansion, Dependency Relations.Abstract
In many applications related to opinion mining and sentiment classification, it is necessary to compute the semantic orientation of certain opinion expressions on an object. Many researchers suggest that semantic orientation depends on application domains. Moreover, semantic orientation depends on the specific feature that an opinion is expressed on it. In this paper, we introduce an effective approach to opinion lexicon expansion automatically. We use small set of seed lexicon and dependency relations to extract opinion words and then, we expand it automatically from a larger set of unannotated documents. To do this, we proposed an unsupervised algorithm based on double propagation. Our method was evaluated in three different domains (headphones, hotels and car), using a corpus of product reviews which opinions were annotated at the feature level. We conclude that our method produces feature-level opinion lexicons with better precision and recall that domain-independent opinion lexicons without using annotated documents.
Downloads
References
Bing Liu, (2011) “Web Data Mining, Springer”, Second Edition, Department of Computer Science, University of Illinois, Chicago, USA
C.J. Hutto and Eric Gilbert, 2014, “VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Texts”, Association for the Advancement of Artificial Intelligent AAAI, www.aaai.org, 2014,.
Esuli, Andrea and Fabrizio Sebastiani. 2005. Determining the semantic orientation of terms through gloss classification. In Proceedings of CIKM’05, pages 617–624.
Guang Qiu, Bing Liu, Jiajun Bu and Chun Chen. "Opinion Word Expansion and Target Extraction through Double Propagation." Computational Linguistics, March 2011, Vol. 37, No. 1: 9.27.
Hatzivassiloglou, Vasileios and Kathleen R. McKeown. 1997. Predicting the semantic orientation of adjectives. In Proceedings of ACL’97, pages 174–181.
Hu, Mingqing and Bing Liu. 2004. Mining and summarizing customer reviews. In Proceedings of SIGKDD’04, pages 168–177.
Kamps, Jaap, Maarten Marx, Robert J. Mokken, and Maarten de Rijke. 2004. Using wordnet to measure semantic orientation of adjectives. In Proceedings of LREC’04, pages 1115–1118.
Kanayama, Hiroshi and Tetsuya Nasukawa. 2006. Fully automatic lexicon expansion for domain-oriented sentiment analysis. In Proceedings of EMNLP’06, pages 355–363.
Kim, Soo-Min and Eduard Hovy. 2004. Determining the sentiment ofopinions. In Proceedings of COLING’04, pages 1367–1373.
Kobayashi, Nozomi, Kentaro Inui, and Yuji Matsumoto. 2007. Extracting aspect-evaluation and aspect-of relations in opinion mining. In Proceedings of EMNLP’07.
Maria Pontiki et al. (2016) “Aspect Based Sentiment Analysis”, Association for Computational Linguistics (ACL), San Diego, California, June 16-17, 2016.
Qian Liu, Zhiqiang Gao, Bing Liu and Yuanlin Zhang. A Logic Programming Approach to Aspect Extraction in Opinion Mining. Proceedings of IEEE/WIC/ACM International Confernece on Web Intelligence (WI-2013), 2013.
Qian Liu, Zhiqiang Gao, Bing Liu and Yuanlin Zhang. “Automated Rule Selection for Aspect Extraction in Opinion Mining.” Proceedings of International Joint Conference onArtificial Intelligence (IJCAI-2015), July 25-31, 2015.
Takamura, Hiroya, Takashi Inui, and Manabu Okumura. 2005. Extracting semantic orientations of words using spin model. In Proceedings of ACL’05, pages 133–140.
Turney, Peter D. and Michael L. Littman. 2003. Measuring praise and criticism: Inference of semantic orientation from association. 21(4):315–346.[16]Wiebe, Janyce. 2000. Learning subjective adjective from corpora. In Proceedings of AAAI’00, pages 735–740.
Wiebe, Janyce, TheresaWilson, Rebecca Bruce, Matthew Bell, and Melanie Martin. 2004. Learning subjective language. 30(3):277–308.
Zhao et al. (2014) “Joint Propagation and Refinement for Mining Opinion Words and Targets”, IEEE Data Mining Workshop, 2014, pp.417-424
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2018 International Journal For Research In Advanced Computer Science And Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
In consideration of the journal, Green Publication taking action in reviewing and editing our manuscript, the authors undersigned hereby transfer, assign, or otherwise convey all copyright ownership to the Editorial Office of the Green Publication in the event that such work is published in the journal. Such conveyance covers any product that may derive from the published journal, whether print or electronic. Green Publication shall have the right to register copyright to the Article in its name as claimant, whether separately
or as part of the journal issue or other medium in which the Article is included.
By signing this Agreement, the author(s), and in the case of a Work Made For Hire, the employer, jointly and severally represent and warrant that the Article is original with the author(s) and does not infringe any copyright or violate any other right of any third parties, and that the Article has not been published elsewhere, and is not being considered for publication elsewhere in any form, except as provided herein. Each author’s signature should appear below. The signing author(s) (and, in
