{"title":"Lexicon-Based Sentiment Analysis for Stock Movement Prediction","authors":"Zane Turner, Kevin Labille, Susan Gauch","volume":169,"journal":"International Journal of Economics and Management Engineering","pagesStart":149,"pagesEnd":155,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10011795","abstract":"<p>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.<\/p>\r\n","references":"[1]\tR. Batra, S. M. Daudpota, \u201cIntegrating StockTwits with sentiment analysis for better prediction of stock price movement,\u201d in 2018 International Conf. on Computing, Mathematics and Engineering Technologies, pp. 1-5.\r\n[2]\tG. K. Basak, P. K. Das, S. Marjit, D. Mukherjee, and L. Yang, \u201cBritish Stock Market, BREXIT and Media Sentiments-A Big Data Analysis,\u201d unpublished.\r\n[3]\tL. Deng, J. Wiebe, \u201cMpqa 3.0: An entity\/event-level sentiment corpus,\u201d in Proc. conf. of the North American chapter of the association for computational linguistics: human language technologies, 2015, Minnesota, pp. 1323-1328.\r\n[4]\tA. Abbasi, A. Hassan, and M. Dhar, \u201cBenchmarking Twitter Sentiment Analysis Tools,\u201d LREC, vol. 14, pp. 26-31, May 2014.\r\n[5]\tT. Loughran, B. McDonald, \u201cWhen is a liability not a liability? Textual analysis, dictionaries, and 10\u2010Ks\u201d. The Journal of Finance, vol. 66, no.1, pp. 35-65, Feb. 2011.\r\n[6]\tE. Henry, \u201cAre investors influenced by how earnings press releases are written?,\u201d The Journal of Business Communication, vol. 45, no. 4, pp. 363-407, Oct. 2008.\r\n[7]\tA. Derakhshan, H. Beigy, \u201cSentiment analysis on stock social media for stock price movement prediction,\u201d Engineering Applications of Artificial Intelligence, vol. 85, pp. 569-578, Oct. 2019.\r\n[8]\tI. Dunder, M. Pavlovski, \u201cComputational concordance analysis of fictional literary work,\u201d MIPRO, In 2018 41st International Conv. on Information and Communication Technology, Electronics and Microelectronics, pp. 644-648.\r\n[9]\tY. Yiran, S. Srivastava, \u201cAspect-based Sentiment Analysis on mobile phone reviews with LDA,\u201d in Proc. 4th International Conf. on Machine Learning Technologies, Austria, 2019, pp. 101-105.\r\n[10]\tA. Muhammad, N. Wiratunga, and R. Lothian, \u201cA hybrid sentiment lexicon for social media mining,\u201d in 2014 IEEE 26th International Conference on Tools with Artificial Intelligence, pp. 461-468.\r\n[11]\tJ. Patel, S. Shah, P. Thakkar, and K. Kotecha, \u201cPredicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques,\u201d Expert Systems with Applications, vol. 42, no. 1, pp. 259-268, Jan. 2015.\r\n[12]\tH. Hu, L. Tang, S. Zhang, and H. Wang, \u201cPredicting the direction of stock markets using optimized neural networks with Google Trends,\u201d Neurocomputing, vol. 285, pp. 188-195, Apr. 2015.\r\n[13]\tD. Hirshleifer, T. Shumway, \u201cGood day sunshine: stock returns and the weather,\u201d The Journal of Finance, vol. 58, no. 3, pp. 1009-1032, Jun. 2013.\r\n[14]\tM. Makrehchi, S. Shah, and W. Liao, \u201cStock prediction using event-based sentiment analysis,\u201d in Proc. IEEE\/WIC\/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, Georgia, 2013, vol. 1, pp. 337-342.\r\n[15]\tR. Akita, A. Yoshihara, T. Matsubara, and K. Uehara. \u201cDeep learning for stock prediction using numerical and textual information,\u201d In 2016 IEEE\/ACIS 15th International Conference on Computer and Information Science, pp. 1-6.\r\n[16]\tT. Matsubara, R. Akita, and K. Uehara, \u201cStock Price Prediction by Deep Neural Generative Model of News Articles,\u201d IEICE TRANSACTIONS on Information and Systems, vol. 101, no. 4, pp. 901-908, Apr. 2018.\r\n[17]\tY. Kim, S. R. Jeong, and I. Ghandi, \u201cText opinion mining to analyze news for stock market prediction,\u201d int. J. Advance. Soft Comput. Appl, vol. 6, no. 1, pp. 2074-2087, Mar. 2014.\r\n[18]\tN. Pr\u00f6llochs, S. Feuerriegel, and D. Neumann, \u201cGenerating Domain-Specific Dictionaries using Bayesian Learning,\u201d in 2015 conf. ECIS, Paper 144.\r\n[19]\tK. Labille, S. Gauch, and S. Alfarhood, \u201cCreating domain-specific sentiment lexicons via text mining\u201d in WISDOM Proc. Workshop Issues Sentiment Discovery Opinion Mining, Halifax, 2017.\r\n[20]\tS. Baccianella, A. Esuli, and F. Sebastiani, \u201cSentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining,\u201d in LREC, Vol. 10, No. 2010, pp. 2200-2204, May 2010.\r\n[21]\tS. Tan, X. Cheng, Y. Wang, and H. Xu, \u201cAdapting naive bayes to domain adaptation for sentiment analysis,\u201d in 2009 European Conference on Information Retrieval, pp. 337-349.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 169, 2021"}