Definition of a Linguistic Resource for Opinion Mining

Franco Tuveri, Manuela Angioni

Abstract

Many approaches to Opinion Mining are based on linguistic resources, lexicons or lists of words. The lack of suitable and/or available resources is one of the main problems in the process of opinion extraction and in general in the analysis of textual resources based on a linguistic approach. In this paper we describe FreeWordNet, a linguistic resource based on WordNet and useful in the automatic method we propose for the extraction of features in a general domain. In FreeWordNet each synset is enriched with a set of properties related to adjectives and adverbs and has a positive, negative or objective value associated. The properties associated to each synset support a better identification of the sentiment expressed in relation to the domain and give more details about the relevant terms or the expressions having an opinion associated.

References

  1. Benamara, F., Cesarano, C., Picariello, A., Reforgiato, D., Subrahmanian, V. S.: Sentiment Analysis: Adjectives and Adverbs are better than Adjectives Alone. In Proceedings of International Conference on Weblogs and Social Media, pp. 203-206 (2007).
  2. Rentoumi, V., Giannakopoulos, G.: Sentiment analysis of figurative language using a word sense disambiguation approach. In International Conference on Recent Advances in Natural Language Processing, Borovets, Bulgaria, ACL (2009).
  3. Miller, G., A.: WordNet: A Lexical Database for English. Communications of the ACM Vol. 38, No. 11: 39-41 (1995).
  4. Lee, D., Jeong, O., Lee, S.: Opinion Mining of customer feedback data on the web. In Proceedings of the 2nd ICUIMC 7808 (2008).
  5. Wiebe, J., Mihalcea, R.: Word Sense and Subjectivity. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, Sydney, Australia (2006).
  6. Esuli, A., Sebastiani, F.: PageRanking WordNet synsets: An application to Opinion Mining. Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics Vol. 45, Publisher: Association for Computational Linguistics, p. 424-431 (2007).
  7. Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. In Proceedings of LREC-10, 7th Conference on Language Resources and Evaluation, Valletta, MT, pages 2200-2204 (2010).
  8. Agerri, R., GarcĂ­a-Serrano, A.: Q-WordNet: Extracting polarity from WordNet senses. Seventh Conference on International Language Resources and Evaluation (2010).
  9. Valitutti, A., Strapparava, C., Stock, O.: Developing affective lexical ressources. Psychnology: 2 (2004).
  10. Magnini, B., Strapparava, C., Pezzulo, G., Gliozzo, A.: 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 (2002).
  11. Cerini, S., Compagnoni, V., Demontis, A., Formentelli, M., Gandini, C.: Micro-WNOp: A gold standard for the evaluation of automatically compiled lexical resources for opinion mining. A. Sanso, Language resources and linguistic theory, Franco Angeli, Italy (2007).
  12. Angioni, M., Demontis, R., Tuveri, F.: A Semantic Approach for Resource Cataloguing and Query Resolution. Communications of SIWN. Special Issue on Distributed Agentbased Retrieval Tools. (2008).
  13. Akkaya, C., Mihalcea, R., Wiebe, J.: Subjectivity Word Sense Disambiguation. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 190-199, Singapore, ACL and AFNLP (2009).
  14. Schmid, H.: Probabilistic Part-of-Speech Tagging Using Decision Trees. In Proceedings of the International Conference on New Methods in Language Processing, pp. 44-49 (1994).
  15. Leacock, C., Chodorow, M.: Combining local context and WordNet similarity for word sense identification. In Fellbaum, C. (ed.)1998, pp. 265-283 (1998).
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Paper Citation


in Harvard Style

Tuveri F. and Angioni M. (2012). Definition of a Linguistic Resource for Opinion Mining . In Proceedings of the 9th International Workshop on Natural Language Processing and Cognitive Science - Volume 1: NLPCS, (ICEIS 2012) ISBN 978-989-8565-16-7, pages 64-73. DOI: 10.5220/0004093300640073


in Bibtex Style

@conference{nlpcs12,
author={Franco Tuveri and Manuela Angioni},
title={Definition of a Linguistic Resource for Opinion Mining},
booktitle={Proceedings of the 9th International Workshop on Natural Language Processing and Cognitive Science - Volume 1: NLPCS, (ICEIS 2012)},
year={2012},
pages={64-73},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004093300640073},
isbn={978-989-8565-16-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Workshop on Natural Language Processing and Cognitive Science - Volume 1: NLPCS, (ICEIS 2012)
TI - Definition of a Linguistic Resource for Opinion Mining
SN - 978-989-8565-16-7
AU - Tuveri F.
AU - Angioni M.
PY - 2012
SP - 64
EP - 73
DO - 10.5220/0004093300640073