FSAL: Lexicón financiero de sentimiento en español rioplatense diseñado para “Bolsas y Mercados Argentinos” (BYMA)

Juan Pablo Braña; Alejandra M. J. Litterio; Alejandro Fernández

doi:10.59471/raia201843

Autores/as

Juan Pablo Braña Centro de Altos Estudios en Tecnología Informática (CAETI)- Universidad Abierta Interamericana, Argentina
Alejandra M. J. Litterio Centro de Altos Estudios en Tecnología Informática (CAETI). Universidad Abierta Interamericana, Argentina
Alejandro Fernández Centro de Altos Estudios en Tecnología Informática (CAETI) . Universidad Abierta Interamericana, Argentina

DOI:

https://doi.org/10.59471/raia201843

Palabras clave:

Análisis de sentimiento, lexicón financiero, Trading algorítmico, Machine learning

Resumen

En la última década, se ha estudiado cómo el Análisis de Sentimiento basado en lexicones en combinación con técnicas de Machine Learning puede ser utilizado para optimizar estrategias de Trading Algorítmico. El presente trabajo tiene como objetivo mostrar que un lexicón de dominio específico en finanzas (FSAL) diseñado para Bolsas y Mercados Argentinos obtiene mejores resultados que un lexicón de propósitos generales (SDAL). Primero, proponemos un lexicón a medida en finanzas. Segundo, mostramos que nuestro lexicón supera los resultados obtenidos en comparación a los resultados de un lexicón de propósitos generales aplicado sobre un corpus compuesto por tweets de cuentas de comunidades de confianza de los mercados argentinos, previamente clasificado de manera colaborativa por expertos en finanzas. Luego, realizamos un estudio comparado de los lexicones aplicando diferentes técnicas de Machine Learning. Final- mente, presentamos algunos resultados preliminares y conclusiones.

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» S.Agaian and P. Kolm (2017). Financial Sentiment Analysis Using Machine Learning Techniques in International Journal of Investment Management and Financial Innovations, 3(1):1-9.

» M. Z. Asghar, Khan, A., Ahmad, S., Qasim, M. and I. A. Khan (2017). Lexicon-enhanced sentiment analysis framework using rule-based classification scheme in PLOS One, 12 (2).

» J. Bollen, H. Mao, and X. Zeng (2011). “Twitter mood predicts the stock market” in Journal of Computational Science, 2(1):1-8.

» Bloom, K. (2011). Sentiment Analysis based on appraisal theory and functional local grammars (Ph. D. Thesis). Graduate College, Illinois Institute of Technology, Chicago. Recuperada de: https://www.scss. tcd.ie/Khurshid.Ahmad/Research/bloom_dissertation.pdf

» E. Brown (2012). “Will twitter make you a better investor? A look at sentiment, user reputation and their effect on the stock market”, in SAIS Proceedings.

» R. Chen and M. Lazer (2011) “Sentiment analysis of twitter feeds for the prediction of stock market movement”.

» E. Ciapuscio, A. Adelstein, L. Brandani, L. Ferrari, S. Gallardo, S. Kornfeld, I. Kuguel y G. Resnik (2009). De la palabra al texto. Estudios lingüísticos del español. Buenos Aires: Eudeba.

» N. P. Cruz, Taboada, M., Mitkov, R. (2016). A machine-learning approach to negation and speculation detection for sentiment analysis. J. Assoc. Inf. Sci. Technol. 67, 2118–2136.

» K. Dave, S. Lawrence and D. M. Pennock. (2003). Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In WWW ’03: Proceedings of the 12th international conference on World Wide Web, pp. 519–528, New York, NY, USA. ACM.

» M. G. Dell’ Amerlina Ríos and M. Gravano (2013). “Spanish DAL: A Spanish Dictionary of Affect in Language”, in Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Atlanta, Georgia, Association for Computational Linguistics, pp. 21–28.

» A. Devitt and K. Ahmad (2007). “Sentiment Polarity Identification in Financial News: A Cohesion-based Approach”, Prague, Czech Republic: Association for Computational Linguistics.

» S. Eggins (2002). Introduction to Systemic Functional Linguistics. London and New York: Pinter Publishers.

» A. Esuli and F. Sebastiani (2006). “SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining,” in Proceedings of the 5th Conference of Language Resource Evaluation, pp. 417–422.

» R. Feldman, B. Rosenfeld, R. Bar-Haim and M. Fresko (2011). “The stock sonar—sentiment analysis of stocks based on a hybrid approach”, in Twenty-Third IAAI Conference.

» P. Finter, A. Niessen-Ruenzi and S. Ruenzi (2012) “The Impact of Investor Sentiment on the German Stock Market”, Springer, vol. 82, no. 2, pp.133-163.

» J.A. García Díaz, Salas-Zárate, M.P., Hernández -Alcaráz, M.L., Valencia-García, R. y J. M. Gómez-Berbíz. (2018). “Machine Learning Based Sentiment Analysis on Spanish Financial Tweets”. In: Rocha Á., Adeli H., Reis L.P., Costanzo S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST’18 2018. Advances in Intelligent Systems and Computing, vol 745. Springer, Cham.

» M. Giammatteo y H. Albano (2006). ¿Cómo se clasifican las palabras? Buenos Aires: Littera Ediciones.

» M. Giammatteo y H. Albano (2009). Lengua, Léxico, Gramática y Texto. Un enfoque para su enseñanza basado en estrategias múltiples. Buenos Aires: Biblos.

» M. Ghiassi and S. Lee (2018). “A domain transferable lexicon set for Twitter Sentiment Analysis using a supervised Machine Learning Approach” in Expert System with Applications, v.106, pp. 197-206. Elsevier.

» M.A.K. Halliday (1994). An Introduction to Functional Grammar. London: Edward Arnold Publishers.

» T. Hendershott, C.M. Jones and A. Menkveld (2011). “Does Algorithmic Trading Improve Liquidity?” in The Journal of Finance, vol. 66, no. 6, pp. 1-34.

» C. Kaushik and A. Mishra (2014). “A scalable, lexicon based technique for Sentiment Analysis”, in the

International Journal in Foundations of Computer Science & Technology (IJFCST), vol.4, no.5.

» C.S.G. Khoo, A. Nourbakhsh and J.C. Na (2014). “Sentiment analysis of online news text: A case study of appraisal theory”, in Online Information Review, 36(6).

» O. Kolchyna, Souza, T.P., Treleaven, P, and Aste, T. (2016) Twitter Sentiment Analyisis: Lexicon Method, Machine Learning Method and Their Combination in the Handbook of Sentiment Analysis in Finance. Mitra, G and Yu, X (Eds.). ISBN 1910571571.

» Kordonis, J., Symeonidis, S., Arampatzis, A. (2016). Stock price forecasting via sentiment analysis on Twitter. In: Proceedings of the 20th Pan-Hellenic Conference on Informatics - PCI 2016. pp. 1–6. ACM Press, New York.

» S. Krishnamoorthy (2017). “Sentiment Analysis of Financial News Articles using performance indicators” in Knowledge and Information System. Springer International Publishing, 1-22.

» A. Kumar, Sethi, A., Akhtar, S., Ekbal, A. , Biemann, C., Bhattacharyya, P., (2017). “IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text” in Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017).

» B. Liu (2015). Sentiment Analysis. Mining Opinions, Sentiments and Emotions. New York: Cambridge University Press.

» H. Mao, P. Gao, Y. Wang and J. Bollen (2014). “Automatic Construction of Financial Semantic Orientation Lexicon from Large-Scale Chinese News Corpus”, in 7th Financial Risks International Forum, vol. 20, no.20, pp. 1-18.

» J. R. Martin and P. R. R. White (2015). The Language of Evaluation, Appraisal in English, London and New York: Palgrave Macmillan.

» J. R. Martin and D. Rose (2003). “Appraisal”, in Working with Discourse. Meaning beyond the clause. London: Continuum.

» A. Moreno-Ortiz y C. Pérez Hernández (2013). “Análisis de Sentimiento basado en lexicones de mensajes de Twitter en Español, en Procesamiento del Lenguaje Natural, Revista nº 50, pp. 93-100.

» A. Muhammad, N. Wiratunga, R. Lothian and R. Glassey (2013). Domain-Based Lexicon Enhancement for Sentiment Analysis, in Proceedings of the BCS SGAI Workshop on Social Media, Cambridge, UK, December 10.

» T. Nashukawa and J. Yi (2003). Sentiment Analysis Capturing Favourability Using Natural Language Processing. K-CAP’03, October 23–25, 2003, Sanibel Island, Florida, USA.

» B. Pang and L. Lee (2008). “Opinion mining and sentiment analysis”, in Foundations and Trends in Information Retrieval, vol. 2, no. 1-2, pp.1–135.

» V. Perez-Rosas, C. Banea and M. Rada (2012). “Learning Sentiment Lexicons in Spanish”, in Proceedings of the Eighth International Conference on Language Resources and Evaluation, vol.12, pp. 3077-3081. European Language Resources Association (ELRA).

» C. Potts (2010). “Sentiment lexicons”, in Linguist 287 / CS 424P: Extracting Social Meaning and Sentiment.

» T. Rao and S. Srivastava (2012). “Analyzing stock market movements using twitter sentiment analysis”, in Proceedings of ASONAM 2012, pp. 119-123.

» Saleiro, P., Mendes Rodrigues, E. , Soares, C. y E. Oliveira (2017). FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings, en Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), pages 904–908.

» H. K. Sul, A.R. Dennis and L. Yuan (2014). “Trading on Twitter: The Financial Information Content of Emotion in Social Media”, in System Sciences (HICSS), 47th Hawaii International Conference.

» M. Taboada, J. Brooke, M. Tofiloski, K. Voll and M. Stede (2011) “Lexicon-Based Methods for Sentiment Analysis”, in Association for Computational Linguistics, vol. 37, no. 2, pp. 267-307.

» Y. R. Tausczik and J. W. (2010). “The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods”, in J. Lang. Soc. Psychol., vol. 29, no. 1, pp. 24–54.

» P. Tedlock (2007). “Giving content to investor sentiment: The role of media in the stock market”, in The Journal of Finance, vol. 62, no. 3, pp. 1139–1168.

» P. Tedlock, C. Maytal Saar-Tsechansky, and S. Macskassy (2008). “More than words: quantifying language to measure firms’ fundamentals”, in The Journal of Finance, 63(3):1437–1467.‌

» A. Tumasjan, T.O. Sprenger, P. G. Sandner and I. M. Welpe (2010). “Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment”, in Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, vol.10, pp. 178-185.

» C. Whissell, M. Fournier, R. Pelland, D. Weir, and K. Makarec (1986). “A dictionary of affect in language” in IV. Reliability, validity, and applications. Perceptual and Motor Skills, vol. 62, no. 3, pp. 875–888.

» C. Whissell (1989). “The dictionary of affect in language”, in Robert Plutchik and Henry Kellerman (ed.) Emotion: Theory, research, and experience, pp.113–131.

» P.R.R. White (2006). “Evaluative Semantics and Ideological Positioning in Journalistic Discourse”, in Image and Ideology in the Mass Media, Lassen, I. (ed.), Amsterdam and Philadelphia: John Benjamins, pp. 45- 73.

» P.R.R. White (2015). “Appraisal Theory”, in The International Encyclopedia of Language and Social Interaction, Tracy, K. (ed.), Ilie, C. and Sandel, T. (Associated Editors), London: John Wiley and Son, Inc.

» M. Zaki and M. Wagner (2014). Data Mining and Analysis. Fundamental Concepts and Algorithms. U.S.A: Cambridge University Press.

» A. Željko, N. Ljubešić and M. Tadić (2010). “Towards Sentiment Analysis of Financial Texts in Croatian”, in Proceedings of the Seventh International Conference on Language Resources and Evaluation. European Language Resources Association.

» L. Zhang (2013). “Sentiment Analysis on Twitter with stock price and significant keyword correlation”, Ph. D. dissertation.

» W. Zhang, and S. Skiena (2010). “Trading strategies to exploit blog and news sentiment”, in The 4th International AAAI Conference on Weblogs and Social Media.

» L. Zhang, R. Ghosh, M. Dekhil, M. Hsu and B. Liu (2011). “Combining Lexicon-based and Learning- based Methods for Twitter Sentiment Analysis”, HP Laboratories, HPL-2011-89.

» X. Zhang (2016). “Positive Discourse Analysis of Attitude System in Economic News”, in International Journal of Arts and Commerce.