A survey on text mining in social networks

• Published in: Knowledge engineering review Vol. 30; no. 2; pp. 157 - 170
• Main Authors: Irfan, Rizwana, King, Christine K., Grages, Daniel, Ewen, Sam, Khan, Samee U., Madani, Sajjad A., Kolodziej, Joanna, Wang, Lizhe, Chen, Dan, Rayes, Ammar, Tziritas, Nikolaos, Xu, Cheng-Zhong, Zomaya, Albert Y., Alzahrani, Ahmed Saeed, Li, Hongxiang
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.03.2015
• Subjects: Algorithms; Boards; Classification; Clustering; Communication; Data mining; Fuzzy; Knowledge engineering; Language; Natural language; Query expansion; Semantics; Social networks; Social research; Text analysis; Texts; Websites
• ISSN: 0269-8889; 1469-8005
• DOI: 10.1017/S0269888914000277

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.