Abstract
We investigated the influence of valence shifters on sentiment analysis within a new model built to extract opinions from economic texts. The system relies on implicit convictions that emerge from the studied texts through co-occurrences of economic indicators and future state modifiers. The polarity of the modifiers can however easily be reversed using negations, diminishers or intensifiers. We compared the system results with and without counting the effect of negations and future state modifier strength and we found that results better than chance are rarely achieved in the second case. In the first case however we proved that the opinion polarity identification accuracy is similar or better than that of other similar tests. Furthermore we found that, when applied to economic indicators, diminishers have the effect of negations.
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References
Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Foundation and Trends in Information Retrieval 2, 1–135 (2008)
Wiebe, J., Wilson, T., Cardie, C.: Annotating Expressions of Opinions and Emotions in Language. Language Resources and Evaluation 39(2-3), 165–210 (2005)
Mei, Q., Ling, X., Wondra, M., Su, H., Zhai, C.: Topic Sentiment Mixture: Modeling Facets and Opinions in Weblogs. In: Proceedings of the 16th International Conference on World Wide Web, pp. 171–180 (2007)
Ferguson, P., O’Hare, M., Bermingham, A.: Exploring the use of Paragraph-level Annotations for Sentiment Analysis of Financial Blogs. In: First Workshop on Opinion Mining and Sentiment Analysis, pp. 42–52 (2009)
Ahmad, K., Cheng, D., Almas, Y.: Multi-lingual sentiment analysis of financial news streams. In: First International Workshop on Grid Technology for Financial Modeling and Simulation, pp. 984–991 (2006)
Ku, L.W., Lee, Y., Wu, T.H., Chen, H.H.: Novel Relationship Discovery Using Opinions Mined from the Web. In: Proceedings of AAAI 2006, pp. 213–221 (2006)
Liu, B.: Opinion Mining. In: Proceedings of WWW 2008, Beijing (2008)
Amigo, E., Spina, D., Bernardino, B.: User Generated Content Monitoring System Evaluation. In: First Workshop on Opinion Mining and Sentiment Analysis, pp. 1–13 (2009)
Cruz, F., Troyano, A., Ortega, F., Enriquez, F.: Domain Oriented Opinion Extraction Metodology. In: First Workshop on Opinion Mining and Sentiment Analysis, pp. 52–62 (2009)
Galbraith, J.: The Affluent Society, ch. 2. Asia Publishing House (1958)
Kennedy, A., Inkpen, D.: Sentiment Classification of Movie Reviews Using Contextual Valence Shifters. Journal of Computational Intelligence 22, 110–125 (2006)
Jia, L., Yu, C., Meng, W.: The effect of negation on sentiment analysis and retrieval effectiveness. In: Proceeding of the 18th ACM Conference on Information and Knowledge Management, pp. 1827–1830 (2009)
Pang, B., Lee, L.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL 2002 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86 (2002)
Popescu, A.M., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 339–346 (2005)
Stavrianou, A., Chauchat, J.H.: Opinion Mining Issues and Agreement Identification in Forum Texts. In: FODOP 2008, pp. 51–58 (2008)
Felbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)
Ding, X., Liu, B., Yu, P.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the International Conference on Web Search and Web Data Mining, pp. 231–240 (2008)
Musat, C., Trausan-Matu, S.: Opinion Mining on Economic Texts. In: First Workshop on Opinion Mining and Sentiment Analysis, pp. 62–72 (2009)
Economy Watch, www.economywatch.com/
Ceglowski, M., Coburn, A., Cuadrado, J.: Semantic Search of Unstructured Data using Contextual Network Graphs (2003)
Blei, D., Ng, A., Jordan, M.: Latent Dirichlet Allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Stone, P.J., Dunphy, D.C., Smith, M.S., Ogilvie, D.M.: The General Inquirer: A computer Approach to Content analysis. MIT Press, Cambridge (1966)
Balahur, A., Steinberger, R.: Rethinking Sentiment Analysis in the News. In: First workshop on Opinion Mining and Sentiment Analysis, pp. 79–89 (2009)
Porter, M.: An Algorithm for Suffix Stripping. In: New Models in Probabilistic Information Retrieval. British Library, London (1980)
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Musat, C., Trausan-Matu, S. (2010). The Impact of Valence Shifters on Mining Implicit Economic Opinions. In: Dicheva, D., Dochev, D. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2010. Lecture Notes in Computer Science(), vol 6304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15431-7_14
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DOI: https://doi.org/10.1007/978-3-642-15431-7_14
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