Text Mining Classification, Clustering, and Applications

Recent years have witnessed an immense growth in the amount of textual information available, both on the World Wide Web and in institutional document repositories. In this context, text mining has become extremely prevalent, giving rise to an age where vast amounts of textual information can be accessed, analyzed, and processed in a fraction of a second. The benefits of text mining go well beyond search and have yielded innovations that help people better understand and make use of the information in document repositories. The development of new technologies to tackle problems such as topic detection, tracking, and trending””where a machine automatically identifies emergent topics in a text corpus””is bound to have wide application in the future. Such applications can be found in ubiquitous consumer-based applications as well as systems focused on banking and finance, health care, aerospace, manufacturing, and the natural sciences.