Chinese Academy of Sciences, 100864 China e-mail: lzwang@ceode.ac.cn, cz.xu@siat.ac.cn, nikolaos@siat.ac.cn"/> King Abdulaziz University, 21589 Saudi Arabia e-mail: asalzahrani@kau.edu.sa"/>
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RESEARCH ARTICLE   Open Access    

A survey on text mining in social networks

  • 1.

    Fargo

  • 2.

    44000 Islamabad

  • 3.

    30001 Cracow

  • 4.

    Chinese Academy of Sciences, 100864 China e-mail: lzwang@ceode.ac.cn, cz.xu@siat.ac.cn, nikolaos@siat.ac.cn

  • 5.

    430000 Wuhan

  • 6.

    San Jose

  • 7.

    2006 NSW

  • 8.

    King Abdulaziz University, 21589 Saudi Arabia e-mail: asalzahrani@kau.edu.sa

  • 9.

    40292 KY

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  • Abstract: In this survey, we review different text mining techniques to discover various textual patterns from the social networking sites. Social network applications create opportunities to establish interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, comments, and discussion boards. Data in social networking websites is inherently unstructured and fuzzy in nature. In everyday life conversations, people do not care about the spellings and accurate grammatical construction of a sentence that may lead to different types of ambiguities, such as lexical, syntactic, and semantic. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several surveys have been conducted to analyze different methods for the information extraction. Most of the surveys emphasized on the application of different text mining techniques for unstructured data sets reside in the form of text documents, but do not specifically target the data sets in social networking website. This survey attempts to provide a thorough understanding of different text mining techniques as well as the application of these techniques in the social networking websites. This survey investigates the recent advancement in the field of text analysis and covers two basic approaches of text mining, such as classification and clustering that are widely used for the exploration of the unstructured text available on the Web.
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  • Cite this article

    Rizwana Irfan, Christine K. King, Daniel Grages, Sam Ewen, Samee U. Khan, Sajjad A. Madani, Joanna Kolodziej, Lizhe Wang, Dan Chen, Ammar Rayes, Nikolaos Tziritas, Cheng-Zhong Xu, Albert Y. Zomaya, Ahmed Saeed Alzahrani, Hongxiang Li. 2015. A survey on text mining in social networks. The Knowledge Engineering Review 30(2)157−170, doi: 10.1017/S0269888914000277
    Rizwana Irfan, Christine K. King, Daniel Grages, Sam Ewen, Samee U. Khan, Sajjad A. Madani, Joanna Kolodziej, Lizhe Wang, Dan Chen, Ammar Rayes, Nikolaos Tziritas, Cheng-Zhong Xu, Albert Y. Zomaya, Ahmed Saeed Alzahrani, Hongxiang Li. 2015. A survey on text mining in social networks. The Knowledge Engineering Review 30(2)157−170, doi: 10.1017/S0269888914000277
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RESEARCH ARTICLE   Open Access    

A survey on text mining in social networks

  • 1.

    Fargo

  • 2.

    44000 Islamabad

  • 3.

    30001 Cracow

  • 4.

    Chinese Academy of Sciences, 100864 China e-mail: lzwang@ceode.ac.cn, cz.xu@siat.ac.cn, nikolaos@siat.ac.cn

  • 5.

    430000 Wuhan

  • 6.

    San Jose

  • 7.

    2006 NSW

  • 8.

    King Abdulaziz University, 21589 Saudi Arabia e-mail: asalzahrani@kau.edu.sa

  • 9.

    40292 KY

  • Published online: 25 March 2015
The Knowledge Engineering Review  30,  2015, 30(2): 157−170  |  Cite this article

Abstract: Abstract: In this survey, we review different text mining techniques to discover various textual patterns from the social networking sites. Social network applications create opportunities to establish interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, comments, and discussion boards. Data in social networking websites is inherently unstructured and fuzzy in nature. In everyday life conversations, people do not care about the spellings and accurate grammatical construction of a sentence that may lead to different types of ambiguities, such as lexical, syntactic, and semantic. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several surveys have been conducted to analyze different methods for the information extraction. Most of the surveys emphasized on the application of different text mining techniques for unstructured data sets reside in the form of text documents, but do not specifically target the data sets in social networking website. This survey attempts to provide a thorough understanding of different text mining techniques as well as the application of these techniques in the social networking websites. This survey investigates the recent advancement in the field of text analysis and covers two basic approaches of text mining, such as classification and clustering that are widely used for the exploration of the unstructured text available on the Web.

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    Acknowledgments

    • We are grateful to Juan Li, Matthew Warner, and Daniel Grages for their feedback on draft of this survey report. Samee U. Khan's work was partly supported by the Young International Scientist Fellowship of the Chinese Academy of Sciences, (Grant No. 2011Y2GA01).

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    Rizwana Irfan, Christine K. King, Daniel Grages, Sam Ewen, Samee U. Khan, Sajjad A. Madani, Joanna Kolodziej, Lizhe Wang, Dan Chen, Ammar Rayes, Nikolaos Tziritas, Cheng-Zhong Xu, Albert Y. Zomaya, Ahmed Saeed Alzahrani, Hongxiang Li. 2015. A survey on text mining in social networks. The Knowledge Engineering Review 30(2)157−170, doi: 10.1017/S0269888914000277
    Rizwana Irfan, Christine K. King, Daniel Grages, Sam Ewen, Samee U. Khan, Sajjad A. Madani, Joanna Kolodziej, Lizhe Wang, Dan Chen, Ammar Rayes, Nikolaos Tziritas, Cheng-Zhong Xu, Albert Y. Zomaya, Ahmed Saeed Alzahrani, Hongxiang Li. 2015. A survey on text mining in social networks. The Knowledge Engineering Review 30(2)157−170, doi: 10.1017/S0269888914000277
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