Sentic Computing for Social Media Analysis, Representation, and Retrieval

Abstract

As the web is rapidly evolving, web users are evolving with it. In the era of social colonisation, people are getting more and more enthusiastic about interacting, sharing and collaborating through social networks, online communities, blogs, wikis and other online collaborative media. In recent years, this collective intelligence has spread to many different areas in the web, with particular focus on fields related to our everyday life such as commerce, tourism, education, and health. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. To overcome such obstacle, we need to explore more concept-level approaches that rely more on the implicit semantic texture of natural language, rather than its explicit syntactic structure. To this end, we further develop and apply sentic computing tools and techniques to the development of a novel unified framework for social media analysis, representation and retrieval. The proposed system extracts semantics from natural language text by applying graph mining and multidimensionality reduction techniques on an affective common sense knowledge base and makes use of them for inferring the cognitive and affective information associated with social media.