Abstract
In recent years there has been an increasing interest in techniques for dealing with the compositionality of meaning. In fact, to derive the meaning of complex expressions (i.e. phrases and sentences) from the meanings of their parts has grabbed the researchers’ attention. In this paper, we examine semantic composition from the perspective of sentiment composition: if the meaning of a sentence is a function of the meanings of its parts, the polarity of a sentence is a function of the polarities of its parts. Basically, we propose a model based on sentential sentiment composition in order to categorise a text review (i.e. an opinion) according to the polarity of the sentences it contains. The experimental results showed that our approach is a plausible alternative to categorise subjective texts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Since the sentiment classes are represented by a rank from 0 to 4, the value to be assigned to the threshold must be given in this rank. The best threshold values in our experimentation were: Books (2.60), Computers (2.45), Movies (2.65) and Music (2.75).
References
Partee, B., Meulen, A., Wall, R.: Mathematical Methods in Linguistics. Kluwer, Dordrecht (1990)
Moilanen, K., Pulman, S.: Sentiment composition. In: Proceedings of Recent Advances in Natural Language Processing (2007)
Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60 (2014)
Harris, Z.: Mathematical Structures of Language. Wiley, New York (1968)
Mitchell, J., Lapata, M.: Vector-based models of semantic composition. In: Proceedings of the 46th Annual Meeting of the ACL, Columbus, OH, pp. 236–244 (2008)
Mitchell, J., Lapata, M.: Composition in distributional models of semantics. Cogn. Sci. 34, 1388–1429 (2010)
Guevara, E.: Computing semantic compositionality in distributional semantics. In: Proceedings of the Ninth International Conference on Computational Semantics, pp. 135–144 (2011)
Taboada, M., Anthony, C., Voll, K.: Creating semantic orientation dictionaries, pp. 427–432 (2006)
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C., Ng, A., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of EMNLP (2013)
Pang, B., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of ACL, pp. 115–124 (2005)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of HLT/EMNLP 2005 (2005)
Blitzer, J., Dredze, M., Pereira, F.: Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification (2007)
Xia, T., Chai, Y.: An improvement to TF-IDF: term distribution based term weight algorithm. J. Softw. 6(3), 413–420 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Uribe, D. (2016). Text Categorisation by Using Sentiment Composition. In: Quesada, J., Martín Mateos, FJ., Lopez-Soto, T. (eds) Future and Emergent Trends in Language Technology. FETLT 2015. Lecture Notes in Computer Science(), vol 9577. Springer, Cham. https://doi.org/10.1007/978-3-319-33500-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-33500-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-33499-8
Online ISBN: 978-3-319-33500-1
eBook Packages: Computer ScienceComputer Science (R0)