Word sense disambiguation application in sentiment analysis of news headlines: an applied approach to FOREX market prediction
- 155 Downloads
Sentiment analysis of textual content has become a popular approach for market prediction. However, lack of a process for word sense disambiguation makes it questionable whether the sentiment expressed by the context is correctly identified. Meanwhile, many studies in natural language processing have focused on word sense disambiguation. However, there has been a weak link between the two logically relevant fields of study. Therefore, with two motivations, we propose a system for FOREX market prediction that exploits word sense disambiguation in sentiment analysis of news headlines and predicts the directional movement of a currency pair. The first motivation is the implementation of a novel word sense disambiguation that can determine the proper senses of all significant words in a news headline. The main contributions of this work that make the first motivation possible, are the introduction of novel approaches termed Relevant Gloss Retrieval, Similarity Threshold, Verb Nominalization, and also optimization measures to decrease execution time. The second motivation is to prove that determination of proper senses of significant words in textual contents can improve the determination of sentiment, conveyed by the context, and consequently any application based on sentiment analysis. Inclusion of the word sense disambiguation into the proposed system proves the achievement of the second motivation. Carried out tests with the same dataset prove that the proposed system outperforms one of the best systems (to our best knowledge) proposed for market prediction and improves accuracy from 83.33% to 91.67%. The detail for reproduction of the system is amply provided.
KeywordsSentiment analysis Semantic analysis Polysemous word Word sense disambiguation FOREX prediction
We want to thank Arman Khadjeh Nassirtoussi (Nassirtoussi et al. 2015) for letting us have access to their data. Without using the same data, we could not compare the results.
- Baccianella, S., Esuli, A., Sebastiani, F. (2010). Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In LREC (Vol. 10 pp. 2200–2204).Google Scholar
- Banerjee, S., & Pedersen, T. (2002). An adapted lesk algorithm for word sense disambiguation using wordnet. In International conference on intelligent text processing and computational linguistics (pp. 136–145). Berlin: Springer.Google Scholar
- Farooq, U., Dhamala, T.P., Nongaillard, A., Ouzrout, Y., Qadir, M.A. (2015). A word sense disambiguation method for feature level sentiment analysis. In 2015 9th international conference on software, knowledge, information management and applications (SKIMA) (pp. 1–8). IEEE.Google Scholar
- Fellbaum C. (1998). WordNet. Wiley Online Library.Google Scholar
- Howe, D.C. (2009). Rita: creativity support for computational literature. In Proceedings of the seventh ACM conference on creativity and cognition (pp. 205–210). ACM.Google Scholar
- Jiang, J.J., & Conrath, D.W. (1997). Semantic similarity based on corpus statistics and lexical taxonomy. arXiv:cmp-lg/9709008.
- Lesk, M. (1986). Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In Proceedings of the 5th annual international conference on systems documentation (pp. 24–26). ACM.Google Scholar
- Levinson, M. et al. (2014). The economist guide to financial markets: why they exist and how they work. The Economist, 1, 17. 6th edition.Google Scholar
- Li, X., Szpakowicz, S., Matwin, S. (1995). A wordnet-based algorithm for word sense disambiguation. In IJCAI (Vol. 95 pp. 1368–1374).Google Scholar
- Lin, D. et al. (1998). An information-theoretic definition of similarity. In Icml, (Vol. 98 pp. 296–304).Google Scholar
- Liu, Y., Scheuermann, P., Li, X., Zhu, X. (2007). Using wordnet to disambiguate word senses for text classification. In Computational Science–ICCS 2007 (pp. 781–789).Google Scholar
- Luk, A. (1993). Statistical sense disambiguation with relatively small corpora using dictionary definitions. In Proceedings of the 33rd annual meeting of ACL (pp. 181–188).Google Scholar
- Miller, G., & Fellbaum, C. (1998). Wordnet: an electronic lexical database. MIT Press, Cambridge.Google Scholar
- Mittermayer, M.A. (2004). Forecasting intraday stock price trends with text mining techniques. In Proceedings of the 37th annual Hawaii international conference on system sciences, 2004 (p. 10). IEEE.Google Scholar
- Nassirtoussi, A.K., Aghabozorgi, S., Wah, T.Y., Ngo, D.C.L. (2014). The text mining homepage. sites.google.com/site/bigdatasetmining/Projects/textmining.
- Patwardhan, S., Banerjee, S., Pedersen, T. (2007). Umnd1: unsupervised word sense disambiguation using contextual semantic relatedness. In Proceedings of the 4th international workshop on semantic evaluations, association for computational linguistics (pp. 390–393).Google Scholar
- Peramunetilleke, D., & Wong, R.K. (2002). Currency exchange rate forecasting from news headlines. Australian Computer Science Communications, 24(2), 131–139.Google Scholar
- Rao, T., & Srivastava, S. (2012). Using twitter sentiments and search volumes index to predict oil, gold, forex and markets indices. Tech. rep.Google Scholar
- Resnik, P. (1995). Using information content to evaluate semantic similarity in a taxonomy. arXiv:cmp-lg/9511007.
- Sul, H.K., Dennis, A.R., Yuan, L.I. (2014). Trading on Twitter: the financial information content of emotion in social media. In 2014 47th Hawaii international conference on system sciences (HICSS) (pp. 806–815). IEEE.Google Scholar
- Toutanova, K., Klein, D., Manning, C.D., Singer, Y. (2003). Feature-rich part-of-speech tagging with a cyclic dependency network. In Proceedings of the 2003 conference of the North American chapter of the association for computational linguistics on human language technology (Vol. 1 pp. 173–180). Association for Computational Linguistics.Google Scholar
- Wilson, T., Wiebe, J., Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the conference on human language technology and empirical methods in natural language processing (pp. 347–354). Association for Computational Linguistics.Google Scholar
- Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd annual meeting on association for computational linguistics (pp. 189–196). Association for Computational Linguistics.Google Scholar