1. bookVolume 2 (2020): Issue 1 (December 2020)
Journal Details
License
Format
Journal
First Published
20 Oct 2019
Publication timeframe
1 time per year
Languages
English
access type Open Access

Use of Social Networks in Determining stockmarket Evolution

Published Online: 31 May 2021
Page range: 94 - 106
Journal Details
License
Format
Journal
First Published
20 Oct 2019
Publication timeframe
1 time per year
Languages
English
Abstract

This article aims to use text mining methods and sentiment analysis to determine the stock market evolution of companies as well as virtual currencies such as Bitcoin. The source of the text is the social media channel Twitter and the text is composed of individual messages sent by users. Although previous papers proved with a degree of certainty that this paper hypothesis is true, as we will see bellow, the area of research was focused only on the professional environment or known opinion makers and not taking into account a high population mass. To ensure that a high level of information is maintained after the sentiment analysis process, we will use multiple algorithms based on different calculation methods and different word dictionaries. In addition, indicators such as the number of assessments, the number of replays etc. will be added to the methodology. By the end of the paper we will be able to both identify a working methodology of analyzing text for the purposes of stock market prediction and also we will touch on the limitations faced when creating it and the ways through which we can expand and improve it’s reliability. The implementation of all these methods and of the multiple dictionaries helped us in simulating human behavior and the differences of opinion, when a group wants to analyze a text. The algorithm becoming a way to balance the different “opinions” that resulted out of the sentiment analysis.

Keywords

Anonim. (n.d.). Databases in R. Retrieved from Databases in R: https://db.rstudio.com/odbc/Search in Google Scholar

Anonim. (n.d.). Python (programming ‘language). Retrieved from Python (programming language): https://en.wikipedia.org/wiki/Python_%28programming_language%29Search in Google Scholar

Anonim. (n.d.). The R Project for Statistical Computing. Retrieved from The R Project for Statistical Computing: https://www.r-project.org/Search in Google Scholar

Baum, C. F. (n.d.). ECON2228. Retrieved from ECON2228: http://fmwww.bc.edu/ECC/F2014/2228/ECON2228_2014_8.slides.pdfSearch in Google Scholar

Bose, S. (2018). Package _RSentiment_. Retrieved from Package _RSentiment_: https://cran.r-project.org/web/packages/RSentiment/RSentiment.pdfSearch in Google Scholar

Brown, A. (2017). How Social Media Affects the Markets. Retrieved from How Social Media Affects the Markets: https://www.financemagnates.com/forex/bloggers/social-media-affects-markets/Search in Google Scholar

Cindi, H., James, R., Rita, S., & Austin, K. (2019). 2019 Magic Quadrant for Analytics and Business Intelligence Platforms. Retrieved from 2019 Magic Quadrant for Analytics and Business Intelligence Platforms: https://www.qlik.com/us/gartner-magic-quadrant-business-intelligenceSearch in Google Scholar

Edanz. (n.d.). Research Point: ANOVA Explained. Retrieved from Research Point: ANOVA Explained: https://www.edanzediting.com/blogs/statistics-anova-explainedSearch in Google Scholar

F. Audrino, F. S., & Ballinari, D. (2019). The impact of sentiment and attention measures on stock market volatility. International Journal of Forecasting. doi:10.1016/j.ijforecast.2019.05.010Search in Google Scholar

Hang, N., Roger, C., & Ranjani, K. (2017). Influence of Social Media Emotional Word of Mouth on Institutional Investors_ Decisions and Firm Value. MANAGEMENT SCIENCE, 1–24. doi:10.1287/mnsc.2018.3226Search in Google Scholar

HLT. (n.d.). SentiWords. Retrieved from SentiWords: https://hlt-nlp.fbk.eu/technologies/sentiwordsSearch in Google Scholar

Isaic-Maniu, A., Mitrut, C., & Voineagu, V. (2002). Statistica Generala. Independenta Economica.Search in Google Scholar

Jia-Yen Huang, J.-H. L. (2019). Using social media mining technology to improve stock price forecast accuracy. MANAGEMENT SCIENCE. doi:10.1002/for.2616Search in Google Scholar

Jockers, M. (n.d.). Retrieved from http://www.matthewjockers.net/Search in Google Scholar

Jon. (2016). TweetScraper. Retrieved from TweetScraper: https://github.com/jonbakerfish/TweetScraperSearch in Google Scholar

Julia, S., & Robinson, D. (2019). Sentiment analysis with tidy data. Retrieved from Sentiment analysis with tidy data: https://www.tidytextmining.com/sentiment.htmlSearch in Google Scholar

Mittal, A., & Goel, A. (2017). Stock Prediction Using Twitter Sentiment Analysis. Retrieved from Stock Prediction Using Twitter Sentiment Analysis: http://cs229.stanford.edu/proj2011/GoelMittal-StockMarketPredictionUsingTwitterSentimentAnalysis.pdfSearch in Google Scholar

NCSS. (n.d.). Stepwise Regression. Retrieved from Stepwise Regression: https://ncsswpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Stepwise_Regression.pdfSearch in Google Scholar

Renault, T. (2017). Intraday online investor sentiment and return patterns in the U.S. stock market. Journal of Banking & Finance. doi:10.1016/j.jbankfin.2017.07.002Search in Google Scholar

Rubbaniy, G., Asmeron (Robel), R., Rizvi (Syed Kumail Abbas), S., & B., N. (2014). Do fear indices help predict stock returns? Quantitative Finance, 14. doi:10.1080/14697688.2014.884722Search in Google Scholar

Schmidt, D. (n.d.). MeanR. Retrieved from MeanR: https://github.com/wrathematics/meanrSearch in Google Scholar

Shri, B., & Angelina, G. (2017). Sentiment Analysis for Effective Stock Market Prediction. International Journal of Intelligent Engineering and Systems, 10(3), 146–154. doi:10.22266/ijies2017.0630.16Search in Google Scholar

Stanford. (n.d.). Sentiment Analysis. Retrieved from Sentiment Analysis: https://nlp.stanford.edu/sentiment/Search in Google Scholar

Techopedia. (n.d.). Web Scraping. Retrieved from Web Scraping: https://www.techopedia.com/definition/5212/web-scrapingSearch in Google Scholar

Wanga, Y.-H., Keswanib, A., & Taylorc, S. J. (2006). The relationships between sentiment, returns and volatility. International Jurnal of Forecasting. doi:10.1016/j.ijforecast.2005.04.019Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo