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Mining opinions from messenger

Published: 24 November 2009 Publication History

Abstract

Increasing Internet users has created enormous important information of users in Internet. Opinion mining is technology that extracts meaningful opinions from that huge information. Becoming a hot research area, opinion mining has been studied in many different ways. These studies are mostly based on reviews, blogs. However, this paper focuses on messenger which generates many messages containing opinions of users. As messages may contain many opinions unrelated to our purpose, our aim is to extract only related opinions and features. Our approach initially collects messages from messengers and employs localized linguistic technique to extract candidate messages, opinions and features. Thereafter, we extract features from candidate features using association rule mining. Finally we summarize extracted opinions and features.

References

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Pang, B., and Lee, L., "Opinion mining and sentiment analysis," Foundations and Trends in Information Retrieval. Al 2, No 1--2, pp. 1--135, 2008.
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Pavel Smrž, "Using WordNet for Opinion Mining," GWC 2006 Proceedings, pp. 333--335, 2006.
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Dey, L and S K Mirajul Haque, "Opinion Mining from Noisy Text Data, "Proc. of the second workshop on Analytics for noisy unstructured text data, pp 83--90, Singapore, 2008.
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Agrawal, R. and Srikant, R. 1994. "Fast algorithm for mining association rules." VLDB'94, 1994.
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Tan, P., Steinbach, M., and Kumar, V. 2006. Introduction to Data Mining. Association Analysis: Basic Concept and Algorithms, 327--404.
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Stanford Tagger Version 1.6, 2008 http://www-nlp.staford.edu/software/tagger.shtml
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WordNet--About WordNet http://wordnet.princeton.edu
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Stanford Parser Version 1.6. 2008. http://nlp.stanford.edu/software/lex-parser.shtml
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Hu, M., and Liu, B., "Mining and summarizing customer reviews," Proc. of the 10th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 168--177, Seatle, Wa, USA, 2004.

Cited By

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  • (2013)Comparative study of a Hybrid Model for network traffic identification and its optimization using Firefly Algorithm2013 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC.2013.6755057(000862-000867)Online publication date: Jul-2013
  • (2013)Applying fuzzy sets for opinion mining2013 International Conference on Computer Applications Technology (ICCAT)10.1109/ICCAT.2013.6521965(1-5)Online publication date: Jan-2013

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cover image ACM Other conferences
ICIS '09: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
November 2009
1479 pages
ISBN:9781605587103
DOI:10.1145/1655925
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 November 2009

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Author Tags

  1. Apriori rule mining
  2. POSTagger
  3. ParseTree
  4. data mining
  5. messenger
  6. opinion mining

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Cited By

View all
  • (2013)Comparative study of a Hybrid Model for network traffic identification and its optimization using Firefly Algorithm2013 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC.2013.6755057(000862-000867)Online publication date: Jul-2013
  • (2013)Applying fuzzy sets for opinion mining2013 International Conference on Computer Applications Technology (ICCAT)10.1109/ICCAT.2013.6521965(1-5)Online publication date: Jan-2013

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