Abstract
We propose a method of assigning polarity to causal information extracted from Japanese financial articles concerning business performance of companies. Our method assigns polarity (positive or negative) according to business performance to causal information, e.g. “zidousya no uriage ga koutyou: (Sales of cars are good)” (The polarity positive is assigned in this example.). First, our method classifies articles concerning business performance into positive articles and negative articles. Using this classified sets of articles, our method assigns polarity (positive or negative) to causal information extracted from the set of articles concerning business performance. We evaluated our method and it attained 75.3% precision and 47.9% recall of assigning polarity positive, and 77.0% precision and 58.5% recall of assigning polarity negative, respectively.
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
Baron, F. and Hirst, G.: Collocations as Cues to Semantic Orientation, AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications (AAAI-EAAT’2004) (2004).
Kaji, N. and Kitsuregawa, M.: Building Lexicon for Sentiment Analysis from Massive Collection of HTML Documents, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 1075–1083 (2007).
Koppel, M. and Shtrimberg, I.: Good News or Bad News? Let the Market Decide, Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text, pp. 86–88 (2004).
Lavrenko, V., Schmill, M., Lawrie, D. and Ogilvie, P.: Mining of Concurrent Text and Time Series, Proceedings of the KDD 2000 Conference Text Mining Workshop (2001).
Sakai, H. and Masuyama, S.: Cause Information Extraction from Financial Articles Concerning Business Performance, IEICE Trans. Information and Systems, Vol. E91-D, No. 4, pp. 959–968 (2008).
Smadja, F: Retrieving collocations from text: Xtract, Computational Linguistics, Vol. 19, No. 1, pp. 143–177 (1993).
Takamura, H., Inui, T. and Okumura, M.: Latent Variable Models for Semantic Orientation of Phrases, Proceedings of the IIth Conference of the European Chapter of the Association for Computational Linguistics (EACL2006), pp. 201–208 (2006).
Turney, P. D.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. Proceedings of 40th Annual Meeting of the Association for Computational Linguistics (ACL2002), pp. 417–424 (2002).
Wilson, T., Wiebe, J. and Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. Proceedings of joint conference on Human Language Technology / Conference on Empirical Methods in Nutura! Language Processing (HLT/EMNLP’05), pp. 347–354 (2005).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag London Limited
About this paper
Cite this paper
Sakai, H., Masuyama, S. (2009). Polarity Assignment to Causal Information Extracted from Financial Articles Concerning Business Performance of Companies. In: Bramer, M., Petridis, M., Coenen, F. (eds) Research and Development in Intelligent Systems XXV. SGAI 2008. Springer, London. https://doi.org/10.1007/978-1-84882-171-2_22
Download citation
DOI: https://doi.org/10.1007/978-1-84882-171-2_22
Publisher Name: Springer, London
Print ISBN: 978-1-84882-170-5
Online ISBN: 978-1-84882-171-2
eBook Packages: Computer ScienceComputer Science (R0)