Natural Language Processing for Social Media

Abstract

This book presents the state-of-the-art in research and empirical studies in the field of Natural Language Processing (NLP) for the semantic analysis of social media data. Over the past few years, online social networking sites have revolutionized the way we communicate with individuals, groups and communities, and altered everyday practices. The unprecedented volume and variety of user-generated content and the user interaction network constitute new opportunities for understanding social behavior and building socially intelligent systems. Much research work on social networks and the mining of the social web is based on graph theory. That is apt because a social structure is made up of a set of social actors and a set of the dyadic ties between these actors. We believe that the graph-mining methods for structure, information diffusion or influence spread in social networks needs to combined with the content analysis of social media. This provides the opportunity for new applications that use the information publicly available as a result of social interactions. The intended audience of this book is researchers who are interested in developing tools and applications for automatic analysis of social media texts. We assume that the readers have basic knowledge in the area of natural language processing and machine learning. This book will help the readers better understand computational linguistics and social media analysis, in particular text-mining techniques and NLP applications (such as summarization, localization detection, sentiment and emotion analysis, topic detection and machine translation) designed specifically for social media texts.