What the future holds for social media data analysis
Article
Article Title | What the future holds for social media data analysis |
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Article Category | Article |
Authors | Wlodarczak, P. (Author), Soar, J. (Author) and Ally, M. (Author) |
Journal Title | International Journal of Computer, Information, Systems and Control Engineering |
Journal Citation | 9 (1), pp. 16-19 |
Number of Pages | 4 |
Year | 2015 |
Place of Publication | Connecticut, CT. United States |
Web Address (URL) | http://www.waset.org/Publications/?path=Publications&p=97 |
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 |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
461199. Machine learning not elsewhere classified | |
460908. Information systems organisation and management | |
Public Notes | © 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. |
Byline Affiliations | School of Management and Enterprise |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q2w69/what-the-future-holds-for-social-media-data-analysis
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