Semi-supervised learning for big social data analysis

In an era of social media and connectivity, web users are becoming increasingly enthusiastic about interacting, sharing, and working together through online collaborative media. More recently, this collective intelligence has spread to many different areas, with a growing impact on everyday life, such as in education, health, commerce and tourism, leading to an exponential growth in the size of the social Web. However, the distillation of knowledge from such unstructured Big data is, an extremely challenging task. Consequently, the semantic and multimodal contents of the Web in this present day are, whilst being well suited for human use, still barely accessible to machines. In this work, we explore the potential of a novel semi-supervised learning model based on the combined use of random projection scaling as part of a vector space model, and support vector machines to perform reasoning on a knowledge base. The latter is developed by merging a graph representation of commonsense with a linguistic resource for the lexical representation of affect. Comparative simulation results show a significant improvement in tasks such as emotion recognition and polarity detection, and pave the way for development of future semi-supervised learning approaches to big social data analytics.