AN AUTOMATIC APPROACH TO FEATURE EXTRACTION

Manuela Angioni, Franco Tuveri

2012

Abstract

The pervasive diffusion of social networks as common way to communicate and share information is becoming a valuable resource for analysts and decision makers. Reviews are used every day by common people or by companies who need to make decisions. It is evident that even the opinion monitoring is essential for listening to and taking advantage of the conversations of possible customers in a decision making process. Opinion Mining is a way to analyse opinions related to specific topics: products, services, tourist locations, etc. In this paper we propose an automatic approach to the extraction of feature terms, applying our experience in the semantic analysis of textual resources to Opinion Mining task and performing a contextualisation by means of semantic categorisation, and by a set of qualities associated to the sense expressed by adjectives and adverbs.

References

  1. Akkaya, C., Wiebe, J., Mihalcea, R., 2009. Subjectivity
  2. Benamara, F., Cesarano, C., Picariello, A., Reforgiato, D., Subrahmanian, V. S., 2007. Sentiment Analysis: Adjectives and Adverbs are better than Adjectives Alone. In Proceedings of ICWSM 07 International Conference on Weblogs and Social Media.
  3. Crimson Hexagon, 2009. Listen, Understand, Act. How a listening platform provides actionable insight. http:// www.crimsonhexagon.com/PDFs/Crimson_Hexagon_ Listen_Understand_Feb_2009.pdf
  4. Ding, X., Liu, B., Yu, P. S., 2008. A Holistic LexiconBased Approach to Opinion Mining. WSDM 7808 Proceedings of the international conference on Web search and web data mining, ACM, New York, USA.
  5. Esuli, A. Sebastiani, F., 2007. PageRanking WordNet synsets: An application to Opinion Mining. Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics Volume: 45, Issue: June, Publisher: Association for Computational Linguistics, Pages: 424-431
  6. Gartner, 2011. Gartner's 2011 Hype Cycle Special Report Evaluates the Maturity of 1,900 Technologies. http:// www.gartner.com/it/page.jsp?id=1763814
  7. Leacock C. and Chodorow M. 1998. Combining local context and WordNet similarity for word sense identification. In Fellbaum 1998, pp. 265-283.
  8. Lee, D., Jeong, O. R., Lee, S., 2008. Opinion Mining of customer feedback data on the web. In ICUIMC 7808 Proceedings of the 2nd international conference on Ubiquitous information management communication
  9. Magnini, B., Strapparava, C., Pezzulo, G., Gliozzo, A., 2002. The Role of Domain Information in Word Sense Disambiguation. Natural Language Engineering, special issue on Word Sense Disambiguation, 8(4), pp. 359-373, Cambridge University Press
  10. Miller, G., 1998. WordNet: An Electronic Lexical Database, Bradford Books
  11. Popescu, A. M., Etzioni, O, 2005. Extracting Product Features and Opinions from Reviews. Proceedings of the 2005 Conference on Empirical Methods in Natural Language Processing.(EMNLP'05).
  12. Rentoumi, V., Giannakopoulos, G., 2009. Sentiment analysis of figurative language using a word sense disambiguation approach. In International Conference on Recent Advances in Natural Language Processing (RANLP 2009), Borovets, Bulgaria, The Association for Computational Linguistics
  13. Yi, J., Nasukawa, T., Bunescu, R., Niblack, W., 2003. Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In Proceedings of the IEEE International Conference on Data Mining.
  14. Zhai, Z., Liu, B., Xu, H., Jia, P., 2010. Grouping Product Features Using Semi-Supervised Learning with SoftConstraints. In Proceedings of the 23rd International Conference on Computational Linguistics (COLING2010), Beijing, China.
Download


Paper Citation


in Harvard Style

Angioni M. and Tuveri F. (2012). AN AUTOMATIC APPROACH TO FEATURE EXTRACTION . In Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-95-9, pages 473-476. DOI: 10.5220/0003714504730476


in Bibtex Style

@conference{icaart12,
author={Manuela Angioni and Franco Tuveri},
title={AN AUTOMATIC APPROACH TO FEATURE EXTRACTION},
booktitle={Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2012},
pages={473-476},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003714504730476},
isbn={978-989-8425-95-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - AN AUTOMATIC APPROACH TO FEATURE EXTRACTION
SN - 978-989-8425-95-9
AU - Angioni M.
AU - Tuveri F.
PY - 2012
SP - 473
EP - 476
DO - 10.5220/0003714504730476