Abstract
Increasing popularity of Social Media has resulted in the creation of a huge amount of user generated documents. A large number of research works have focused on inferring relationship in certain specific social network domains. Few have considered structured data to establish syntax based relationship. In this work, we develop a two-step syntax based and semantic based relationship mining approach. Here we generalize the concept of relationship mining for all structured as well as unstructured unsupervised text documents from all social network domains. At first, we choose suitable features from individual document and store them in graph structure. Then we establish relationships in the graph generated to obtain Reduced node Social Graph with Relationships (RSGR). Our empirical study on various social media document validates the effectiveness of our approach and suggests its generality in finding relationships irrespective of the type of text documents and the social network domains.
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Sharma, T., Toshniwal, D. (2014). A Generalized Relationship Mining Method for Social Media Text Data. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2014. Lecture Notes in Computer Science(), vol 8556. Springer, Cham. https://doi.org/10.1007/978-3-319-08979-9_28
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DOI: https://doi.org/10.1007/978-3-319-08979-9_28
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