Integrating Social Network Structure into Online Feature Selection

Short-texts accentuate the challenges posed by the high feature space dimensionality of text learning tasks. The linked nature of social data causes new dimensions to be added to the feature space, which, also becomes sparser. Thus, efficient and scalable online feature selection becomes a crucial requirement of numerous large-scale social applications. This thesis proposes an online feature selection technique for high-dimensional data based on both social and content-based information for the real-time classification of short-text streams coming from social media. The main objective of this thesis is to define and evaluate a new intelligent text mining technique for enhancing the process of knowledge discovery in social-media. This technique would help in the development of new and more effective models for personalisation and recommendation of content in social environments.