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.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 118
 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.
 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K. Das, S. Marjit, D. Mukherjee, and L. Yang, “British Stock Market, BREXIT and Media Sentiments-A Big Data Analysis,” unpublished.
 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.
 A. Abbasi, A. Hassan, and M. Dhar, “Benchmarking Twitter Sentiment Analysis Tools,” LREC, vol. 14, pp. 26-31, May 2014.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 N. Pröllochs, S. Feuerriegel, and D. Neumann, “Generating Domain-Specific Dictionaries using Bayesian Learning,” in 2015 conf. ECIS, Paper 144.
 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.
 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.
 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.