What the Future Holds for Social Media Data Analysis
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:68634", author = "P. Wlodarczak and J. Soar and M. Ally", title = "What the Future Holds for Social Media Data Analysis", 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.", volume = "9", number = "1", pages = "16-4", }
