Overview of Text Visualization Techniques

Abstract

The increasing availability of electronic document archives, such as webpages, online news sites, blogs, and various publications and articles, provides an unprecedented amount of information. This situation introduces a new challenge, which is the discovery of useful knowledge from large document collections effectively without completely going through the details of each document in the collection. Information visualization techniques provide a convenient means to summarize documents in visual forms that allow users to fully understand and memorize data insights. In turn, this process facilitates data comparison and pattern recognition. Many text visualization techniques have been extensively studied and developed for different purposes since the 1990s. In this chapter, we briefly review these techniques to provide an overview of text visualization. Our survey is based on studies summarized in the online text visualization browser (http://textvis.lnu.se/). We classify different text visualization techniques regarding their design goals, which largely group existing techniques into five categories. These categories include techniques developed (1) for visualizing document similarity, (2) for revealing content, (3) for visualizing sentiments and emotions of the text, (4) for exploring document corpus, and (5) for analyzing various domain-specific rich-text corpus, such as social media data, online news, emails, poetry, and prose. Based on this taxonomy, we introduce the details of the primary text visualization research topics in the following chapters of this book.