Sentiment Analysis of Twitter Data within Big Data Distributed Environment for Stock Prediction

• Autorzy: Michał Skuza , Andrzej Romanowski
• Języki publikacji: EN

Abstrakty

EN

This paper covers design, implementation and evaluation of a system that may be used to predict future stock prices basing on analysis of data from social media services. The authors took advantage of large datasets available from Twitter micro blogging platform and widely available stock market records. Data was collected during three months and processed for further analysis. Machine learning was employed to conduct sentiment classification of data coming from social networks in order to estimate future stock prices. Calculations were performed in distributed environment according to Map Reduce programming model. Evaluation and discussion of results of predictions for different time intervals and input datasets proved efficiency of chosen approach is discussed here(original abstract)

Słowa kluczowe

PL

Analiza danych; Uczenie maszynowe; Analiza wydźwięku

EN

Data analysis; Machine learning; Sentiment analysis

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2015
• Tom: 5
• Strony: 1349-1354

Twórcy

autor

Michał Skuza

• Lodz University of Technology

autor

Andrzej Romanowski

• Lodz University of Technology

Bibliografia

• Z. Da, J. Engelberg, P. Gao: In Search of Attention, The Journal of Finance Volume 66, Issue 5, pages 1461-1499, October 2011, doi: 10.1111/j.1540-6261.2011.01679.x
• J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A.H. Byers. Big data: The next frontier for innovation, competition, and productivity, McKinsey, May 2011.
• Edd Dumbill. What is big data? : an introduction to the big data landscape. http://radar.oreilly.com/2012/01/what-is-big-data.html, 2012.
• P. Zikopoulos, C.Eaton, D. DeRoos, T. Deutch and G. Lapis, Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Osborne Media, 2011
• M. Zajicek. Web 2.0: hype or happiness? In Proceedings of the 2007 international cross-disciplinary conference on Web accessibility (W4A), W4A '07, pages 35-39, New York, NY, USA, 2007. ACM. doi: 10.1145/1243441.1243453
• T. Ahlqvist and Valtion teknillinen tutkimuskeskus. Social media roadmaps: exploring the futures triggered by social media. VTT tiedotteita. VTT, 2008.
• Twitter Statistics. http://www.statisticbrain.com/twitter-statistics/, 2013. [Online; accessed 2-January-2013].
• B. Pang and L. Lee. Opinion mining and sentiment analysis. Found. Trends Inf. Retr., 2(1-2):1-135, January 2008, doi: 10.1561/1500000011
• Y-W Seo, J.A. Giampapa, and K. Sycara. Text classification for intelligent portfolio management. Technical Report CMU-RI-TR 02- 14, Robotics Institute, Pittsburgh, PA, May 2002.
• A. Esuli and F. Sebastiani. Sentiwordnet: A publicly available lexical resource for opinion mining. In In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC'06, pages 417-422, 2006. In In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC'06, pages 417-422, 2006, doi: 10.1155/2015/715730
• N.N. Taleb, Common Errors in the Interpretation of the Ideas of The Black Swan and Associated Papers (October 18, 2009)
• M. Paluch, L. Jackowska-Strumillo: The influence of using fractal analysis in hybrid MLP model for short-term forecast of closing prices on Warsaw Stock Exchange. Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 2, pages 111-118 (2014) doi: 10.15439/2014F358
• M. Marcellino, J. H. Stock, M.W. Watson, A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series, Journal of Econometrics Volume 135, Issues 1-2, November-December 2006, Pages 499-526 doi:10.1016/j.jeconom.2005.07.020
• Asur, S., Huberman, B.A., Predicting the Future with Social Media IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2010, pp 492 - 499 doi:10.1109/WIIAT. 2010.63
• K-J. Kim, I. Han, Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index, Expert Systems with Applications Volume 19, Issue 2, 2000, Pages 125-132 doi:10.1016/S0957-4174(00)00027-0
• E. J. Ruiz, V. Hristidis, C. Castillo, and A. Gionis, "Correlating Financial Time Series with Micro-Blogging activity," WSDM 2012. Doi: 10.1145/2124295.2124358

Identyfikatory

DOI

10.15439/2015F230