Abstract
The task of opinion mining has attracted interest during the last years. This is mainly due to the vast availability and value of opinions on-line and the easy access of data through conventional or intelligent crawlers. In order to utilize this information, algorithms make extensive use of word sets with known polarity. This approach is known as dictionary-based sentiment analysis. Such dictionaries are available for the English language. Unfortunately, this is not the case for other languages with smaller user bases. Moreover, such generic dictionaries are not suitable for specific domains. Domain-specific dictionaries are crucial for domain-specific sentiment analysis tasks. In this paper we alleviate the above issues by proposing an approach for domain-specific dictionary building. We evaluate our approach on a sentiment analysis task. Experiments on user reviews on digital devices demonstrate the utility of the proposed approach. In addition, we present NiosTo, a software that enables dictionary extraction and sentiment analysis on a given corpus.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Amati, G., Ambrosi, E., Bianchi, M., Gaibisso, C., Gambosi, G.: Automatic construction of an opinion-term vocabulary for ad hoc retrieval. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 89–100. Springer, Heidelberg (2008)
Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: 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, ACL 1998, pp. 174–181. Association for Computational Linguistics, Stroudsburg (1997)
Nasukawa, T., Kanayama, H.: Fully automatic lexicon expansion for domain - oriented sentiment analysis (2006)
Hu, M., Liu, B.: Mining and summarizing customer reviews (2004)
Kanayama, H., NasuKawa, T.: Fully automatic lexicon expansion for domain - oriented sentiment analysis
Kokkoras, F., Ntonas, K., Bassiliades, N.: Deixto: A web data extraction suite. In: Proceedings of the 6th Balkan Conference in Informatics, BCI 2013, pp. 9–12. ACM, New York (2013)
Liu, K., Xu, L., Liu, Y., Zhao, J.: Opinion target extraction using partially-supervised word alignment model. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI 2013, pp. 2134–2140. AAAI Press (2013)
Qiu, G., Liu, B., Bu, J., Chen, C.: Opinion word expansion and target extraction through double propagation. Comput. Linguist. 37(1), 9–27 (2011)
Wiebe, J., Riloff, E.: Learning extraction patterns for subjective expressions. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 105–112 (2003)
Skomorowski, J., Vechtomova, O.: Ad hoc retrieval of documents with topical opinion. In: Amati, G., Carpineto, C., Romano, G. (eds.) ECIR 2007. LNCS, vol. 4425, pp. 405–417. Springer, Heidelberg (2007)
Wu, Y., Zhang, Q., Huang, X., Wu, L.: Phrase dependency parsing for opinion mining. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol. 3, pp. 1533–1541. Association for Computational Linguistics, Stroudsburg (2009)
Yu, P.S., Ding, X., Liu, B.: A holistic lexicon-based approach to opinion mining (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Agathangelou, P., Katakis, I., Kokkoras, F., Ntonas, K. (2014). Mining Domain-Specific Dictionaries of Opinion Words. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2014. WISE 2014. Lecture Notes in Computer Science, vol 8786. Springer, Cham. https://doi.org/10.1007/978-3-319-11749-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-11749-2_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11748-5
Online ISBN: 978-3-319-11749-2
eBook Packages: Computer ScienceComputer Science (R0)