A Survey of Data Mining Techniques for Social Media Analysis
Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for info...
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| Vydané v: | Journal of data mining and digital humanities Ročník 2014 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Nicolas Turenne
24.06.2014
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| Predmet: | |
| ISSN: | 2416-5999, 2416-5999 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | Social network has gained remarkable attention in the last decade. Accessing
social network sites such as Twitter, Facebook LinkedIn and Google+ through the
internet and the web 2.0 technologies has become more affordable. People are
becoming more interested in and relying on social network for information, news
and opinion of other users on diverse subject matters. The heavy reliance on
social network sites causes them to generate massive data characterised by
three computational issues namely; size, noise and dynamism. These issues often
make social network data very complex to analyse manually, resulting in the
pertinent use of computational means of analysing them. Data mining provides a
wide range of techniques for detecting useful knowledge from massive datasets
like trends, patterns and rules [44]. Data mining techniques are used for
information retrieval, statistical modelling and machine learning. These
techniques employ data pre-processing, data analysis, and data interpretation
processes in the course of data analysis. This survey discusses different data
mining techniques used in mining diverse aspects of the social network over
decades going from the historical techniques to the up-to-date models,
including our novel technique named TRCM. All the techniques covered in this
survey are listed in the Table.1 including the tools employed as well as names
of their authors. |
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| ISSN: | 2416-5999 2416-5999 |
| DOI: | 10.46298/jdmdh.5 |