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In Sinclair and Rockwell’s Text Analysis and Word Visualization: Making Meaning Count , the authors highlight the importance and efficiency of text analysis. Throughout the article, Sinclair and Rockwell provide examples of how the use of these semantic programs are effectively able to produce information on digital texts that exist. Specifically, they describe how visualizations are able to help collect important data, “Visualizations are transformations of text that tend to reduce the amount of information presented, but in service of drawing attention to some significant aspect.” Although this is extremely valuable information, it is only made significant by the individual examining the data. While text analysis is important, the data that is collected can only be used if it interpreted by the user. In Sinclair and Rockwells’s interactive NFL example, only the individual can determine the significant information that emphasizes the racial discrepancies in player description. A classmate of mine succinctly poses this question , “How is a computer to know what items are of importance and know better than a human would?” These are essential questions that are necessary for the development of semantic and text analysis. While I think creating a program that is able to differentiate this significant information is next to impossible, the extraction of analysis in extremely complicated and long texts is very important.