What the Future Holds for Social Media Data Analysis

Authors: P. Wlodarczak, J. Soar, M. Ally

Abstract:

The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques.

Keywords: Social Media, text mining, knowledge discovery, predictive analysis, machine learning.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1337671

References:

[1] B. Liu, Sentiment Analysis and Opinion Mining. CA: Morgan & Claypool Publishers, 2012, ch. 1.
[2] S. Asur, and B. A. Huberman, “Predicting the Future with Social Media,” in Conf. Rec. 2010 IEEE Int. Conf. Web Intelligence, pp. 492– 499.
[3] E. Siegel, Predictive analytics. Hoboken, NJ: John Wiley & Sons, 2013, pp. 11.
[4] M. Arias, A. Arraita, and R. Xuriquera, “Forecasting with twitter data,” ACM Trans. Intell. Syst. Technol., vol. 5, no. 1, pp. 1-24, Dec. 2013.
[5] A. Tumasjan, I. M. Welpe, P. G. Sandner, A. Tumasjan, and T. O. Sprenger, 'Election Forecasts With Twitter: How 140 Characters Reflect the Political Landscape', Social science computer review, vol. 29, no. 4, 2011, pp. 402-18.
[6] J. Bollen, H. Mao, and X. J. Zeng, 'Twitter mood predicts the stock market', Journal of Computational Science, 2010, vol. 2, p. 8.
[7] H. Achrekar, A. Gandhe, R. Lazarus, Y. Ssu-Hsin, and L. Benyuan, 'Predicting Flu Trends using Twitter data', in Computer Communications Workshops (INFOCOM WKSHPS), 2011 IEEE Conference on, 2011, pp. 702-7.
[8] T. Sakaki, M. Okazaki, and Y. Matsuo, 'Earthquake shakes Twitter users: real-time event detection by social sensors', Proc. of the 19th international conference on World wide web, Raleigh, 2010.
[9] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, 'Lexiconbased methods for sentiment analysis', Comput. Linguist., 2011, vol. 37, no. 2, pp. 267-307.
[10] K. Tretyakov, 'Machine Learning Techniques in Spam Filtering', in Data Mining Problem-oriented Seminar, MTAT.03.177, 2004, Estonia.
[11] P. Gundecha, and H. Liu, 'Mining Social Media: A Brief Introduction', informs, 2012, vol. 9, pp. 1-17.
[12] I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn, Morgan Kaufmann Publishers, Burlington, USA, 2011.
[13] I. Maks, and P. Vossen, 'A lexicon model for deep sentiment analysis and opinion mining applications', Decis. Support Syst., 2012, vol. 53, no. 4, pp. 680-8.
[14] C. Diks, and V. Panchenko, 'A new statistic and practical guidelines for nonparametric Granger causality testing', Journal of Economic Dynamics and Control, 2006, vol. 30, no. 9–10, pp. 1647-69.
[15] K. Zhang, 'Big social media data mining for marketing intelligence', 3563913 thesis, Northwestern University, via ProQuest Dissertations & Theses A&I, 2013.
[16] D. Power, JP. Daniel and P-W. Gloria, 'Impact of Social Media and Web 2.0 on Decision-Making', Journal of decision systems, 2011, vol. 20, no. 3, p. 249.
[17] H. Schoen, D. Gayo-Avello, PT. Metaxas, E. Mustafaraj, M. Strohmaier and P. Gloor, 'The power of prediction with social media', Internet Research, 2013, vol. 23, no. 5, pp. 528 - 43.
[18] KL. Short, 'Buy My Vote: Online Reviews for Sale', Vanderbilt Journal of Entertainment & Technology Law, 2013, vol. 15, no. 2, pp. 441-71.
[19] Z. Bai, W-K. Wong and B. Zhang, 'Multivariate linear and nonlinear causality tests', Mathematics and Computers in Simulation, 2010, vol. 81, no. 1, pp. 5-17.