Extraction and Analysis of Social Network Data Using Text Mining Techniques
T. M. Hayath · International Journal for Research in Applied Science and Engineering Technology · 2017
Social network websites provide a means of communication between people who are located in different locations and this can be done by establishing a network in which the information such as text messages, pictures, audio and videos can be shared.The information that is stored in these websites will be in unstructured manner and hence it may lead to ambiguity such as lexical, semantic, syntax of data.Moreover the data set that is generated from the information pattern is more complex and difficult to analyze.Another problem with these kinds of websites is that there may be a chance of fraudulent activities carried out, such as creating fake profiles.The proposed technique implements pre-processing (tokenization) which removes irrelevant or redundant data which are stored in unstructured manner and then the hybrid classifier is used to detect the fraudulent activities carried out in social networking websites which in turn increases the performance of the system.