Abstract
Subjectivity tagging is a prior step for sentiment annotation. Both machine learning based approaches and linguistic knowledge based ones profit from using subjectivity lexicons. However, most of these kinds of resources are often available only for English or other major languages. This work analyses two strategies for building subjectivity lexicons in an automatic way: by projecting existing subjectivity lexicons from English to a new language, and building subjectivity lexicons from corpora. We evaluate which of the strategies performs best for the task of building a subjectivity lexicon for a less-resourced language (Basque). The lexicons are evaluated in an extrinsic manner by classifying subjective and objective text units belonging to various domains, at document- or sentence-level. A manual intrinsic evaluation is also provided which consists of evaluating the correctness of the words included in the created lexicons.
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
Preview
Unable to display preview. Download preview PDF.
References
Yu, H., Hatzivassiloglou, V.: Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, EMNLP 2003, pp. 129–136 (2003)
Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, ACL 2004 (2004)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of HLT/EMNLP 2005, pp. 347–354 (2005)
Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 105–112 (2003)
Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Language Resources and Evaluation 39(2), 165–210 (2005)
Esuli, A., Sebastiani, F.: SENTIWORDNET: a publicly available lexical resource for opinion mining. In: Proceedings of LREC 2006, pp. 417–422 (2006)
Stone, P., Dunphy, D., Smith, M.: The general inquirer: A computer approach to content analysis (1966)
Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL 2002, Philadelphia, Pennsylvania, p. 417 (2002)
Kaji, N., Kitsuregawa, M.: Building lexicon for sentiment analysis from massive collection of HTML documents. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007, Prague, Czech Republic, pp. 1075–1083 (2007)
Mihalcea, R., Banea, C., Wiebe, J.: Learning multilingual subjective language via cross-lingual projections. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, vol. 45, p. 976 (2007)
Banea, C., Mihalcea, R., Wiebe, J., Hassan, S.: Multilingual subjectivity analysis using machine translation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2008, pp. 127–135 (2008)
Wan, X.: Using bilingual knowledge and ensemble techniques for unsupervised chinese sentiment analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2008, pp. 553–561 (2008)
Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., Cardie, C., Riloff, E., Patwardhan, S.: OpinionFinder. In: Proceedings of HLT/EMNLP on Interactive Demonstrations, Vancouver, British Columbia, Canada, pp. 34–35 (2005)
Quirk, R., Greenbaum, S., Leech, G., Svartvik, J.: A comprehensive grammar of the English language. Pearson Education India (1985)
Wilson, T.A.: Fine-grained Subjectivity and Sentiment Analysis: Recognizing the Intensity, Polarity, and Attitudes of Private States. ProQuest (2008)
Wiebe, J.M., Bruce, R.F., O’Hara, T.P.: Development and use of a gold-standard data set for subjectivity classifications. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, ACL 1999, pp. 246–253 (1999)
Wang, X., Fu, G.H.: Chinese subjectivity detection using a sentiment density-based naive bayesian classifier. In: 2010 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 6, pp. 3299–3304 (2010)
Das, A., Bandyopadhyay, S.: Subjectivity detection in english and bengali: A CRF-based approach. In: Proceeding of ICON (2009)
Das, A., Bandyopadhyay, S.: Theme detection an exploration of opinion subjectivity. In: 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009, pp. 1–6 (September 2009)
Yu, N., Kübler, S.: Filling the gap: Semi-supervised learning for opinion detection across domains. In: Proceedings of the Fifteenth Conference on Computational Natural Language Learning, pp. 200–209 (2011)
Jijkoun, V., de Rijke, M.: Bootstrapping subjectivity detection. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, New York, NY, USA, pp. 1125–1126 (2011)
Riloff, E., Wiebe, J., Wilson, T.: Learning subjective nouns using extraction pattern bootstrapping. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, Edmonton, Canada, pp. 25–32 (2003)
Baroni, M., Vegnaduzzo, S.: Identifying subjective adjectives through web-based mutual information. In: Proceedings of the 7th Konferenz zur Verarbeitung Natürlicher Sprache, KONVENS 2004, pp. 613–619 (2004)
Jijkoun, V., Hofmann, K.: Generating a non-english subjectivity lexicon: relations that matter. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2009, pp. 398–405 (2009)
Banea, C., Mihalcea, R., Wiebe, J.: Multilingual sentiment and subjectivity analysis. In: Multilingual Natural Language Processing (2011)
Wiebe, J., Wilson, T., Bell, M.: Identifying collocations for recognizing opinions. In: Proceedings of the ACL 2001 Workshop on Collocation: Computational Extraction, Analysis and Exploitation, pp. 24–31 (2001)
Maks, I., Vossen, P.: Building a fine-grained subjectivity lexicon from a web corpus. In: Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC 2012), Istanbul, Turkey (May 2012)
Wang, D., Liu, Y.: A cross-corpus study of unsupervised subjectivity identification based on calibrated em. In: Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, WASSA 2011, pp. 161–167 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Saralegi, X., San Vicente, I., Ugarteburu, I. (2013). Cross-Lingual Projections vs. Corpora Extracted Subjectivity Lexicons for Less-Resourced Languages. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2013. Lecture Notes in Computer Science, vol 7817. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37256-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-37256-8_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37255-1
Online ISBN: 978-3-642-37256-8
eBook Packages: Computer ScienceComputer Science (R0)