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Whereas previously we have discussed how data can put forth inaccurate depictions or can be misleading when attempting to draw conclusions, this article demonstrates the use of data in uncovering otherwise hidden insights. As the article highlights, text-based records in particular readily provide the opportunity for analysis and visualization of data.

Visualizations allow for an alternative method of “representing significant features…more compactly and more efficiently…in service of drawing attention to … significant aspect[s].”

From a human perspective, there are two sides to this coin: On one hand, text analysis can be overwhelmingly beneficial in that it can help readers to understand texts that they do not have the time, or perhaps the ability, to otherwise comprehend.  On the other hand, how can text analysis help draw a reader’s attention to items of interest they had not previously noticed? How is a computer to know what items are of importance and know better than a human would? (This dilemma is reminiscent of one a peer mentioned in a previous post concerning text mining, computers tend to privilege informational significance over the aesthetic prose of the test, making it extremely difficult to be able to fully automate the understanding of a text).

Essentially, the computer reads the texts as a series of parts and patterns and it is up to humans to write a code that determines what is “important.” Generally, this would require some preconceived notion of what is important in the text- wouldn’t this ultimately defeat the original purpose of discovering items of interest that the reader had not previously considered? In this sense its interesting to think of the variety of perspectives from which text analysis can be approached (e.g. whether the reader is seeking out a general understanding, details of a specific topic, investigating a topic that could shed light on a factor that was not clearly represented in the text overall etc).