My Midterm Exam Project

For my midterm exam project, I created a visual textual analysis of Shakespeare’ Midsummer Night’s Dream. My only source was the text of the play itself, and the data I used for this project were the words within the text. 

I began by opening the full text of the Midsummer Night’s Dream in the Voyant Tools program. I looked through the most frequently used words in the play and isolated four of what I believed to be the most commonly used “positive” words and the four most commonly used “negative words.” For this process, I only was going off of my own positivity measurements of the words. I also only selected words that are very clearly in one category of the other. There were some words such as “true” that are generally considered positive, but I didn’t use these because true could be used to point out a dangerous or unfortunate situation. The positive words I ultimately decided on were “love” (109 times), “sweet” (48 times), “good” (42 times) and “fair” (33 times). The negative words I chose were “fear” (17 times), “death” (14 times), “dead” (14 times) and “hate” (9 times).

I then graphed these word frequencies in Voyant Tools to see where they occur most commonly throughout the play. Graphs are very easy to create within Voyant Tools and I also think they provide a very clear visual representation of word frequencies. This is why I chose to present my data this way. I first graphed the positive and negative words separately so the viewer can easily see the progression of the frequencies throughout the play and not be overwhelmed by the amount of lines present. 

These graphs are meaningful because they can provide a basic understanding of the plot of the play. For example, one can see that most positive words occur in the beginning and middle of the play, especially love. This suggests a calm and happy exposition. The happy words then decrease in frequency towards the end of the play and the negative word “dead” increases dramatically, suggesting that this play may not have a happy ending. This process is not perfect, especially since there are only four words in each category, but these visuals can still give us useful information of the emotions occurring at each point in the play. 

I also graphed the negative and positive word frequencies together to get a feel for if the text is overall more positive or negative.

By looking at the frequency graph above, we can see that love is the most prevalent word throughout the whole play. The positive words have general higher frequencies than the negative words. Hate spikes at the same time love does in the center of the play. “Dead” has the highest frequency in the final part of the play. Overall, the play seems to be a net positive and highly romantic. 

As I said earlier, this process is not perfect. However, I think applying this technique to a text can be very helpful in giving a broad overview of the emotions, mood and plot points present in a piece of literature.