In Lab 2, I experimented with online tools that help to analyze the text Only Revolutions. I examined both Sam and Hailey’s narrative in the program Lexos, but only looked at Hailey’s narrative for the program Voyant. Both provided visual aids to show the words that were most used in the story.


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  1. In this picture, I used the function “Word CLoud” in Lexos to visually see which words were used most in Only Revolutions. Both Sam and Hailey’s narratives were uploaded in order to do this and then stop words were eliminated. Sam is the most frequently used word and Hailey is the second most frequent.

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  1. The graph shows the two document’s clouds separately, as opposed to a representation of the entire book. Many of the words here are similar in the two narratives with minor differences such as the names.



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  1. In Voyant, this visual represents the words most frequently used in Hailey’s narrative. This is a similar format as the Lexos word cloud, but includes more words. I removed the stop words to find the frequently used unique words in the story. My mouse is over the “i’m” and it shows that it was used 236 times in Hailey’s narrative.

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  1. I used the Word Trends tool here to to visualize the use of the word “i’m” in Hailey’s narrative. It clearly is at its highest usage at the beginning and the end of the story.



Voyant and Lexos are tools that complement the close reading process. Although for many it seems strange to use technology in this way, it allows someone to close read more carefully if he or she understands the trends in the book because the human mind cannot collect the data that a machine can. Close reading is an essential part of reading that most people do not realize they are doing. Only Revolutions has so many patterns to observe and analyze that the reader is very aware that he or she is close reading, while other books are more straightforward and allow for a more casual reading style. Through using machines to analyze Only Revolutions, I learned that Hailey and Sam use similar words throughout the novel. For a book with so many patterns, the computer can help to sift through the data and present it to the reader. From there, the computer does not tell the reader how to interpret that data. Therefore, the machine is only aiding the reader without hindering the close reading process. However, close reading on one’s own offers its own benefits. When tools such as Voyant and Lexos are eliminated in the close reading process, close reading is more natural. In addition, it trains the human mind to pay attention to detail more closely instead of relying on a machine to report the trends. In addition, a machine cannot hear the effect of the sound and rhythm of words in a book. Close reading without machines values sound and sight, which the machine cannot understand. Tone and narrative voice also play an important role in close reading that the machine does not pick up on. Through close reading without a machine, the scientific aspect of reading washes away and the reader is able to be intimate with the text. By recognizing the narrative voice of the story, a reader feels closer to the text. One could argue that tools such as Lexos and Voyant make a reader feel farther from the text because he or she is less involved in the extraction of information. A machine mediates the reading of a text, which may make the reader feel distant from it. Close reading offers a more connected feeling between the reader and the text because there is nothing between them. A person could close read without the help of a machine, however, it does find data in a moment that the human mind would take an enormous amount of time to collect. For this reason, I think that these tools are important in the process of close reading and that they help more than they hurt. In Culler’s article, he discusses the ideas of De Man. De Man believes that an important part of close reading is to have “a respect for the stubbornness of the texts, which resist easy comprehension or description in terms of expect themes and motifs.” Machines do not respect the stubbornness of texts because they cannot respect at all. Using tools to aid close reading is helpful, but it makes the process less human and intimate. Everything is not meant to be understood in a piece of text, which is something that a machine will never understand.