I found this chapter to give some insights on quite an interesting area of research i.e. text analysis. Coupled with an effective data visualization scenario, the topic can be of great interest to any computational linguistic researcher I believe.
Author clearly explains the heuristics of applying data visualizations to the text mining in the beginning. The examples and tools mentioned helped me understand how actually text analysis works. I especially liked the tool TRIST very good as it represents search results as document icons and also supports multiple linked dimensions that allow for finding characteristics and correlations among the documents.
Also, according to the authors Tag Clouds are not a very good way to represent a textual context into a visualizations. However, a few years back there was a wave of bloggers adding Tag Clouds to their blogs. I feel, Tag clouds are effective but with too much information overload, users may get overwhelmed. I think probably that's the reason Twitter now only shows few of the popular hashtags
In the last section of the chapter authors discuss about visualizing literature and citation relationships both of which are closely related to the field of text mining and text concordance analysis . Some of examples were discussed in the class too (NameVoyager visualization)
I certainly feel that the chapter is useful for anyone who's into textual analysis and want to learn some effective visual representation as an addendum. Even the most popular social networks these days are coming up with innovative approach of visual representation of content that user adds (Facebook timeline) and sites like IBM's manyeyes.com which makes visualizations accessible to non tech-savvy users.
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