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Lexicon-Based Sentiment Analysis for Stock Movement Prediction
Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.
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[1] R. Batra, S. M. Daudpota, “Integrating StockTwits with sentiment analysis for better prediction of stock price movement,” in 2018 International Conf. on Computing, Mathematics and Engineering Technologies, pp. 1-5.
[2] G. K. Basak, P. K. Das, S. Marjit, D. Mukherjee, and L. Yang, “British Stock Market, BREXIT and Media Sentiments-A Big Data Analysis,” unpublished.
[3] L. Deng, J. Wiebe, “Mpqa 3.0: An entity/event-level sentiment corpus,” in Proc. conf. of the North American chapter of the association for computational linguistics: human language technologies, 2015, Minnesota, pp. 1323-1328.
[4] A. Abbasi, A. Hassan, and M. Dhar, “Benchmarking Twitter Sentiment Analysis Tools,” LREC, vol. 14, pp. 26-31, May 2014.
[5] T. Loughran, B. McDonald, “When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks”. The Journal of Finance, vol. 66, no.1, pp. 35-65, Feb. 2011.
[6] E. Henry, “Are investors influenced by how earnings press releases are written?,” The Journal of Business Communication, vol. 45, no. 4, pp. 363-407, Oct. 2008.
[7] A. Derakhshan, H. Beigy, “Sentiment analysis on stock social media for stock price movement prediction,” Engineering Applications of Artificial Intelligence, vol. 85, pp. 569-578, Oct. 2019.
[8] I. Dunder, M. Pavlovski, “Computational concordance analysis of fictional literary work,” MIPRO, In 2018 41st International Conv. on Information and Communication Technology, Electronics and Microelectronics, pp. 644-648.
[9] Y. Yiran, S. Srivastava, “Aspect-based Sentiment Analysis on mobile phone reviews with LDA,” in Proc. 4th International Conf. on Machine Learning Technologies, Austria, 2019, pp. 101-105.
[10] A. Muhammad, N. Wiratunga, and R. Lothian, “A hybrid sentiment lexicon for social media mining,” in 2014 IEEE 26th International Conference on Tools with Artificial Intelligence, pp. 461-468.
[11] J. Patel, S. Shah, P. Thakkar, and K. Kotecha, “Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques,” Expert Systems with Applications, vol. 42, no. 1, pp. 259-268, Jan. 2015.
[12] H. Hu, L. Tang, S. Zhang, and H. Wang, “Predicting the direction of stock markets using optimized neural networks with Google Trends,” Neurocomputing, vol. 285, pp. 188-195, Apr. 2015.
[13] D. Hirshleifer, T. Shumway, “Good day sunshine: stock returns and the weather,” The Journal of Finance, vol. 58, no. 3, pp. 1009-1032, Jun. 2013.
[14] M. Makrehchi, S. Shah, and W. Liao, “Stock prediction using event-based sentiment analysis,” in Proc. IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, Georgia, 2013, vol. 1, pp. 337-342.
[15] R. Akita, A. Yoshihara, T. Matsubara, and K. Uehara. “Deep learning for stock prediction using numerical and textual information,” In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science, pp. 1-6.
[16] T. Matsubara, R. Akita, and K. Uehara, “Stock Price Prediction by Deep Neural Generative Model of News Articles,” IEICE TRANSACTIONS on Information and Systems, vol. 101, no. 4, pp. 901-908, Apr. 2018.
[17] Y. Kim, S. R. Jeong, and I. Ghandi, “Text opinion mining to analyze news for stock market prediction,” int. J. Advance. Soft Comput. Appl, vol. 6, no. 1, pp. 2074-2087, Mar. 2014.
[18] N. Pröllochs, S. Feuerriegel, and D. Neumann, “Generating Domain-Specific Dictionaries using Bayesian Learning,” in 2015 conf. ECIS, Paper 144.
[19] K. Labille, S. Gauch, and S. Alfarhood, “Creating domain-specific sentiment lexicons via text mining” in WISDOM Proc. Workshop Issues Sentiment Discovery Opinion Mining, Halifax, 2017.
[20] S. Baccianella, A. Esuli, and F. Sebastiani, “Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining,” in LREC, Vol. 10, No. 2010, pp. 2200-2204, May 2010.
[21] S. Tan, X. Cheng, Y. Wang, and H. Xu, “Adapting naive bayes to domain adaptation for sentiment analysis,” in 2009 European Conference on Information Retrieval, pp. 337-349.
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