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Implicit Aspect Identification Techniques for Mining Opinions: A Survey

by Mily Lal, Kavita Asnani
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 98 - Number 4
Year of Publication: 2014
Authors: Mily Lal, Kavita Asnani
10.5120/17168-7238

Mily Lal, Kavita Asnani . Implicit Aspect Identification Techniques for Mining Opinions: A Survey. International Journal of Computer Applications. 98, 4 ( July 2014), 1-3. DOI=10.5120/17168-7238

@article{ 10.5120/17168-7238,
author = { Mily Lal, Kavita Asnani },
title = { Implicit Aspect Identification Techniques for Mining Opinions: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 98 },
number = { 4 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-3 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume98/number4/17168-7238/ },
doi = { 10.5120/17168-7238 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:25:18.615458+05:30
%A Mily Lal
%A Kavita Asnani
%T Implicit Aspect Identification Techniques for Mining Opinions: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 98
%N 4
%P 1-3
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Gathering information by finding out what other people think is always been a common behavior . It has become necessary to summarize the information obtained due to its growing availability and popularity in the form of online review sites and personal blogs . Aspect extraction is one major step for mining opinions. Extracting aspects still remains to be a challenging in problem in opinion mining domain. Most of the research works have only concentrated in extracting explicit aspects. Implicit aspects are also important because they relate to sellers, services and logistics. Without knowing it mined opinions can be of no use. This paper describes techniques and approaches that promises to enable implicit extraction for opinion seeking systems

References
  1. Turney, p. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews, In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, Pennsylvania.
  2. Pang, B Lee, L. , and Vaithyanathan, S. (2002). Thumbs up? Sentiment Classification Using Machine Learning Techniques, In Proc. of EMNLP.
  3. Hu, M. and Liu, B. 2004. Mining and summarizing customer reviews. International Conference on Knowledge Discovery and Data Mining (ICDM).
  4. Popescu, A. and Etzioni, O. (2005). Extracting product features and opinions from reviews, In Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP).
  5. Ana-Maria Popescu and Oren Etzioni . OPINE: Extracting Product Features and Opinions from Reviews. Proceedings of HLT/EMNLP 2005 Demonstration Abstracts, pages 32–33,Vancouver, October 2005.
  6. Liu, B. , Hu, M. , And Cheng, J. 2005. Opinion observer: analyzing and comparing opinions on the web. In WWW '05: Proceedings of the 14th international conference on World Wide Web. ACM, New York, NY, USA, 342–351.
  7. Ding, X. , Liu, B. and Yu, P. S. (2008). A holistic lexicon-based approach to opinion mining, In Proceedings of the Conference on Web Search and Web Data Mining (WSDM).
  8. Qi Su,Kun Xiang,Houfeng Wang,Bin Sun and Shiwen Yu(2006). Using Pointwise Mutual Information to Identify Implicit Features in Customer Reviews. ICCPOL ,LNAI 4285 ,pp. 22-30,Springer(2006).
  9. Almuhareb, A. 2006. Attributes in Lexical Acquisition. Ph. D. Dissertation, Department of Computer Science, University of Essex.
  10. Zhu J. , H. Wang, M,Zhu and B. K. Tsou. 2011. Aspect based opinion polling from customer reviews. IEEE Transactions on Affective Computing, 2(1):37-49.
  11. Qiu, G. , B. Liu, J. Bu, and C. Chen. "Opinion word expansion and target extraction through double propagation. " Computational Linguistics, 2011.
  12. Jin, W. and H. Ho. "OpinionMiner: a novel machine learning system for web opinion mining and extraction. "In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009), 2009b.
  13. Anaïs Cadilhac, Farah Benamara, Nathalie Aussenac-Gilles . "Ontolexical resources for feature based opinion mining : a case-study. Proceedings of the 6th Workshop on Ontologies and Lexical Resources "(Ontolex 2010), pages 77–86,Beijing, August 2010
  14. Pang B, Lee L. (2008) "Opinion Mining and Sentiment Analysis", Foundations and Trends in Information Retrieval, Vol. 2, Nos. 1-2, pp. 1-135, 2008.
  15. Nilesh M. Shelke, Shriniwas Deshpande, Vilas Thakre ,Survey of Techniques for Opinion Mining, International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 9, September 2013
  16. Yu Zhang, Weixiang Zhu Extracting Implicit Features in Online Customer Reviews for Opinion Mining, WWW '13 Companion Proceedings of the 22nd international conference on World Wide Web companion.
  17. G. Vinodhini* , RM. Chandrasekaran, Sentiment Analysis and Opinion Mining: A Survey, Volume 2, Issue 6,June 2012 International Journal of Advanced Research in Computer Science and Software Engineering
  18. Bing Liu. Sentiment Analysis and Opinion Mining, Morgan & Claypool Publishers, May 2012.
  19. Zhen Hai, Kuiyu Chang and Jung-jae Kim. Implicit Feature Identification via Co-occurrence Association rule Mining. Springer(2011)
  20. LIU, B. , "Sentiment Analysis and Subjectivity" Handbook of Natural Language Processing, Second Edition, (editors: N. Indurkhya and F. J. Damerau), 2010
  21. Wei Wang ,Hua Xu, Wei Wan "Implicit feature identification via hybrid association rule mining" Expert Systems with Applications: An International Journal Volume 40 Issue 9, July, 2013
  22. Hartung, M and A. Frank, 2010. A Structured Vector Space Model for hidden Attribute Meaning in Adjective?Noun Phrases. Coling 2010.
  23. Geli Fei 1 Bing Liu ,"A Dictionary?Based Approach to Identifying Aspects Implied by Adjectives for Opinion Mining". Proceedings of COLING 2012: Posters, pages 309–318,COLING 2012, Mumbai, December 2012
  24. KU, L. -W. , LIANG, Y. -T. , AND CHEN, H. -H. 2006. Opinion extraction, summarization and tracking in news and blog corpora. In AAAI Symposium on Computational Approaches to Analysing Weblogs (AAAI-CAAW). 100–107.
Index Terms

Computer Science
Information Sciences

Keywords

Aspects extraction implicit aspect Opinions sentiments sentiment lexicon sentiment classification

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