A Framework for Mining Speech-to-Text Transcripts of the Customer for Automated Problem Remediation

Text mining is a new and exciting area of computer science research that tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. Similarly, link detection -a rapidly evolving approach to the analysis of text that shares and builds on many of the key elements of text mining -also provides new tools for people to better leverage their burgeoning textual data resources. Link detection relies on a process of building up networks of interconnected objects through various relationships in order to discover patterns and trends. The main tasks of link detection are to extract, discover, and link together sparse evidence from vast amounts of data sources, to represent and evaluate the significance of the related evidence, and to learn patterns to guide the extraction, discovery, and linkage of entities. The Text Mining Handbook presents a comprehensive discussion of the state of the art in text mining and link detection. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, the work examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection in such varied fields as corporate finance business intelligence, genomics research, and counterterrorism activities.