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Aspect-based Opinion Mining: A Survey

by K. Vivekanandan, J. Soonu Aravindan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 106 - Number 3
Year of Publication: 2014
Authors: K. Vivekanandan, J. Soonu Aravindan
10.5120/18501-9566

K. Vivekanandan, J. Soonu Aravindan . Aspect-based Opinion Mining: A Survey. International Journal of Computer Applications. 106, 3 ( November 2014), 21-26. DOI=10.5120/18501-9566

@article{ 10.5120/18501-9566,
author = { K. Vivekanandan, J. Soonu Aravindan },
title = { Aspect-based Opinion Mining: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 106 },
number = { 3 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 21-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume106/number3/18501-9566/ },
doi = { 10.5120/18501-9566 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:38:25.655954+05:30
%A K. Vivekanandan
%A J. Soonu Aravindan
%T Aspect-based Opinion Mining: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 106
%N 3
%P 21-26
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Opinion mining has been an emerging research field in Computational Linguistics, Text Analysis and Natural Language Processing (NLP) in recent years. It is the computational study of people's opinions towards entities and their aspects. Entities usually refer to individuals, events, topics, products and organizations. Aspects are attributes or components of entities. In the last few years, social media has become an excellent source to express and share people's opinion on entities and their aspects. With the availability of vast opinionated web contents in the form of comments, reviews, blogs, tweets, status updates, etc. it is harder for people to analyze all opinions at a time to make good decisions. So, there is a need for effective automated systems to evaluate opinions and generate accurate results. Sentiment Analysis, Emotion Analysis, Subjectivity Detection has also become an active research area in recent years along with opinion mining. This article presents a brief overview of opinion mining and its classifications and specifically focuses on the sub topic aspect-based opinion mining, its approaches, metrics used for evaluation and latest research challenges.

References
  1. Liu, Bing. 2010. "Sentiment Analysis and Subjectivity. " Handbook of Natural Language Processing: 1–38.
  2. Sarvabhotla, Kiran, Prasad Pingali, and Vasudeva Varma. 2011. "Sentiment Classification: A Lexical Similarity Based Approach for Extracting Subjectivity in Documents. " Information Retrieval 14: 337–53.
  3. Turney, Peter D. 2001. "Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. " In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL '02 417- 424.
  4. Goldberg, Andrew, Andrew Goldberg, Xiaojin Zhu, and Xiaojin Zhu. 2006. "Seeing Stars When There Aren't Many Stars: Graph-Based Semi-Supervised Learning for Sentiment Categorization. " In Proceedings of TextGraphs: The First Workshop on Graph Based Methods for Natural Language Processing, 45–52.
  5. Zhang, Zhu, and Balaji Varadarajan. 2006. "Utility Scoring of Product Reviews. " In Proceedings of the 15th ACM International Conference on Information and Knowledge Management, 51–57.
  6. Pang, Bo, and Lillian Lee. 2008. "Opinion Mining and Sentiment Analysis. " Foundations and Trends® in Information Retrieval 2: 1–135.
  7. Pang, Bo, Lillian Lee, and Shivakumar Vaithyanathan. 2002. "Thumbs up? Sentiment Classification Using Machine Learning Techniques. " In Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), 79–86.
  8. Wiebe, Janyce et al. 2004. "Learning Subjective Language. " Computational Linguistics 30: 277–308.
  9. Yu, Lei, Jia Ma, Seiji Tsuchiya, and Fuji Ren. 2008. "Opinion Mining: A Study on Semantic Orientation Analysis for Online Document. " In Proceedings of the World Congress on Intelligent Control and Automation (WCICA), 4548–52.
  10. Lim, Ee-Peng et al. 2010. "Detecting Product Review Spammers Using Rating Behaviors. " Proceedings of the 19th ACM International Conference on Information and Knowledge Management: 939–48.
  11. Liu, Jingjing et al. 2007. "Low-Quality Product Review Detection in Opinion Summarization. " In Computational Linguistics, 334–42.
  12. Moghaddam, Samaneh, and Martin Ester. 2011. "AQA: Aspect-Based Opinion Question Answering. " In Proceedings - IEEE International Conference on Data Mining, ICDM, 89–96.
  13. Hu, Minqing, and Bing Liu. 2004. "Mining and Summarizing Customer Reviews. " Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining KDD 04 04: 168.
  14. Huang, Shen et al. 2009. "Improving Product Review Search Experiences on General Search Engines. " In Proceedings of the 11th International Conference on Electronic Commerce - ICEC '09, 107.
  15. Jindal, Nitin, Bing Liu, and Liu Bing. 2006. "Mining Comparative Sentences and Relations. " In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '06, 1331–36.
  16. Lu, Yue, ChengXiang Zhai, and Neel Sundaresan. 2009. "Rated Aspect Summarization of Short Comments. " Proceedings of the 18th international conference on World wide web - WWW '09: 131.
  17. Wong, Tak-Lam, Wai Lam, and Tik-Shun Wong. 2008. "An Unsupervised Framework for Extracting and Normalizing Product Attributes from Multiple Web Sites. " Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval SIGIR 08: 35.
  18. Popescu, Ana Maria, and Orena Etzioni. 2007. "Extracting Product Features and Opinions from Reviews. " In Natural Language Processing and Text Mining, 9–28.
  19. Liu, Bing, and South Morgan Street. 2005. "Opinion Observer?: Analyzing and Comparing Opinions on the Web. " In Proceedings of the 14th International Conference on World Wide Web, 342–51.
  20. Riloff, Ellen et al. 2003. "Learning Subjective Nouns Using Extraction Pattern Bootstrapping. " Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 -: 25–32.
  21. Hatzivassiloglou, V. , V. Hatzivassiloglou, K. R. McKeown, and K. R. McKeown. 1997. "Predicting the Semantic Orientation of Adjectives. " Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics pages: 181.
  22. Lingwei Zeng and Fang Li. 2013. "A Classification-Based Approach for Implicit Feature Identification. " Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data Lecture Notes in Computer Science: 190-202.
  23. Wang, Wei, Hua Xu, and Wei Wan. 2013. "Implicit Feature Identification via Hybrid Association Rule Mining. " Expert Systems with Applications 40: 3518–31.
  24. Su, Qi et al. 2006. "Using Pointwise Mutual Information to Identify Implicit Features in Customer Reviews. " In Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 22–30.
  25. Scaffidi, Christopher et al. 2007. "Red Opal: Product-Feature Scoring from Reviews. " In Proceedings of the 8th ACM Conference on Electronic Commerce - EC '07, 182.
  26. Baccianella, Stefano, Andrea Esuli, and Fabrizio Sebastiani. 2009. "Multi-Facet Rating of Product Reviews. " In Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 485–96.
  27. Zhuang, Li, Feng Jing, and Xiao-Yan Zhu. 2006. "Movie Review Mining and Summarization. " In Proceedings of the 15th ACM International Conference on Information and Knowledge Management - CIKM '06, 43.
  28. Jin, Wei, Hung Hay Ho, and Rohini K Srihari. 2009. "OpinionMiner: A Novel Machine Learning System for Web Opinion Mining and Extraction. " Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining: 1195–1203.
  29. Wong, Tak-Lam, Lidong Bing, and Wai Lam. 2011. "Normalizing Web Product Attributes and Discovering Domain Ontology with Minimal Effort. " In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, 805–14.
  30. Titov, Ivan, and Ryan McDonald. 2008. "Modeling online reviews with multi-grain topic models. " In Proceedings of the 17th international conference on World Wide Web-WWW '08, 111-120.
  31. Zhao, Wayne Xin, Jing Jiang, Hongfei Yan, and Xiaoming Li. 2010. "Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid. " Computational Linguistics 16: 56–65.
  32. Samaneh Abbasi Moghaddam, (2013), "Aspect-Based Opinion Mining in Online Reviews" (Doctoral Dissertation), Simon Fraser University, Canada.
  33. Gibbs, Raymond W. 1986. "On the Psycholinguistics of Sarcasm. " Journal of Experimental Psychology: General 115: 3–15.
  34. Kreuz, Roger J. , and Sam Glucksberg. 1989. "How to Be Sarcastic: The Echoic Reminder Theory of Verbal Irony. " Journal of Experimental Psychology: General 118: 374–86.
  35. Ekman, P. (1992). An argument for basic emotions. Cognition & Emotion.
Index Terms

Computer Science
Information Sciences

Keywords

Sentiment Analysis Emotion Analysis Subjectivity Detection Polarity Detection Text Analysis.

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