What the future holds for social media data analysis

Wlodarczak, P. and Soar, J. and Ally, M. (2015) What the future holds for social media data analysis. International Journal of Computer, Information, Systems and Control Engineering, 9 (1). pp. 16-19. ISSN 1307-6892

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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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2015 World Academy of Science, Engineering and Technology. Open Access Journal. This publication is copyright. It may be reproduced in whole or in part for the purposes of study, research, or review, but is subject to the inclusion of an acknowledgment of the source.
Faculty / Department / School: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise
Date Deposited: 10 Feb 2015 04:12
Last Modified: 19 Jul 2016 04:38
Uncontrolled Keywords: social media; text mining; knowledge discovery; predictive analysis; machine learning
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
17 Psychology and Cognitive Sciences > 1702 Cognitive Sciences > 170203 Knowledge Representation and Machine Learning
08 Information and Computing Sciences > 0806 Information Systems > 080609 Information Systems Management
Socio-Economic Objective: B Economic Development > 89 Information and Communication Services > 8999 Other Information and Communication Services > 899999 Information and Communication Services not elsewhere classified
URI: http://eprints.usq.edu.au/id/eprint/26553

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