Lexicon-Based Sentiment Analysis for Stock Movement Prediction

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for Stock Movement Prediction</title> <meta name="description" content="Lexicon-Based Sentiment Analysis for Stock Movement Prediction"> <meta name="keywords" content="Computational finance, sentiment analysis, sentiment lexicon, stock movement prediction."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Lexicon-Based Sentiment Analysis for Stock Movement Prediction"> <meta name="citation_author" content="Zane Turner"> <meta name="citation_author" content="Kevin Labille"> <meta name="citation_author" content="Susan Gauch"> <meta name="citation_publication_date" content="2020/04/02"> <meta name="citation_journal_title" content="International Journal of Mechanical and Industrial Engineering"> <meta name="citation_volume" content="14"> <meta name="citation_issue" content="5"> <meta name="citation_firstpage" content="220"> <meta name="citation_lastpage" 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Kevin Labille</a>, <a href="https://publications.waset.org/search?q=Susan%20Gauch"> Susan Gauch</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <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 introduce 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> <iframe src="https://publications.waset.org/10011215.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Computational%20finance" title="Computational finance">Computational finance</a>, <a href="https://publications.waset.org/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/search?q=sentiment%20lexicon" title=" sentiment lexicon"> sentiment lexicon</a>, <a href="https://publications.waset.org/search?q=stock%20movement%20prediction." title=" stock movement prediction."> stock movement prediction.</a> </p> <a href="https://publications.waset.org/10011215/lexicon-based-sentiment-analysis-for-stock-movement-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10011215/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10011215/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10011215/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10011215/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10011215/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10011215/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10011215/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10011215/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10011215/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10011215/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10011215.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1137</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] R. Batra, S. M. Daudpota, “Integrating StockTwits with sentiment analysis for better prediction of stock price movement,” in 2018 Int. Conf. on Computing, Mathematics and Engineering Technologies, pp. 1-5. <br>[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. <br>[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. <br>[4] A. Abbasi, A. Hassan, and M. Dhar, “Benchmarking Twitter Sentiment Analysis Tools,” LREC, vol. 14, pp. 26-31, May 2014. <br>[5] T. Loughran, B. McDonald, “When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks”. The J. of Finance, vol. 66, no.1, pp. 35-65, Feb. 2011. <br>[6] E. Henry, “Are investors influenced by how earnings press releases are written?,” The J. of Business Communication, vol. 45, no. 4, pp. 363-407, Oct. 2008. <br>[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. <br>[8] I. Dunder, M. Pavlovski, “Computational concordance analysis of fictional literary work,” MIPRO, In 2018 41st Int. Conv. on Information and Communication Technology, Electronics and Microelectronics, pp. 644-648. <br>[9] Y. Yiran, S. Srivastava, “Aspect-based Sentiment Analysis on mobile phone reviews with LDA,” in Proc. 4th Int. Conf. on Machine Learning Technologies, Austria, 2019, pp. 101-105. <br>[10] A. Muhammad, N. Wiratunga, and R. Lothian, “A hybrid sentiment lexicon for social media mining,” in 2014 IEEE 26th Int. Conf. on Tools with Artificial Intelligence, pp. 461-468. <br>[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 Syst. with Applications, vol. 42, no. 1, pp. 259-268, Jan. 2015. <br>[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. <br>[13] D. Hirshleifer, T. Shumway, “Good day sunshine: stock returns and the weather,” The J. of Finance, vol. 58, no. 3, pp. 1009-1032, Jun. 2013. <br>[14] M. Makrehchi, S. Shah, and W. Liao, “Stock prediction using event-based sentiment analysis,” in Proc. IEEE/WIC/ACM Int. Joint Conf. on Web Intelligence and Intelligent Agent Technologies, Georgia, 2013, vol. 1, pp. 337-342. <br>[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 Int. Conf. on Computer and Information Science, pp. 1-6. <br>[16] T. Matsubara, R. Akita, and K. Uehara, “Stock Price Prediction by Deep Neural Generative Model of News Articles,” IEICE Transactions on Information and Syst., vol. 101, no. 4, pp. 901-908, Apr. 2018. <br>[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. <br>[18] N. Pröllochs, S. Feuerriegel, and D. Neumann, “Generating Domain-Specific Dictionaries using Bayesian Learning,” in 2015 conf. ECIS, Paper 144. <br>[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. <br>[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. <br>[21] S. Tan, X. Cheng, Y. Wang, and H. Xu, “Adapting naive bayes to domain adaptation for sentiment analysis,” in 2009 European Conf. on Information Retrieval, pp. 337-349. </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>

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Lexicon-Based Sentiment Analysis for Stock Movement Prediction