Blog Post 5: Data Mining and Quantifying Literature

    Quantifying literature can be done incredibly quickly with the help of resources such as Voyant. But what does this information actually tell us? Tools like Voyant help to highlight patterns within a body of text through visualizations like Wordclouds and Bubblelines. I have found these visualizations incredibly helpful especially as a first-time Voyant user. After seeing the most popular words in a text through the Wordcloud, such as in the text I'm analyzing, The Tell-Tale Heart, I can make assumptions about the content of the story, the author's writing style, or what tone/mood that the author is trying to evoke. My Wordcloud shows the most popular words in the story to be "old", "night", "man", and "heard". From this, I can make assumptions that Poe was going for a darker tone in his writing, and that our story takes place at night time, and that one of the characters is an old man. In my opinion, this source was helpful, but pretty confusing. Luckily, my text was not as long as most that are used in distant-reading practices, so I was able to actually read it and get a better understanding of what happened in the story.

    This leads me to my next point which was briefly discussed in chapter 7 of the textbook: can distant reading replace actually reading a text, or is it just a way to mine for statistics? In terms of using these methods for traditional literary research, I can see how they would be helpful. We could use distant reading to compare literary works from different time periods, seeing how writing styles or vocabulary changed over time. We could get better ideas about the cultural shifts that took place over time as well, which I think is the biggest influence that distant reading has. However, in my opinion distant-reading is just a way to generate statistics about the work of literature, and to actually understand the context in which these statistics are important, it requires a human mind to read the works. We need to understand how trends in data can be related to the lived experiences of people in order to put these statistics to use.