I focused on Voyant Tools. Initially, I uploaded two different young adult books separately as the texts to analyze and I noticed most of the frequent words used for both were common words like “said”, “just”, “like” or a character’s name. I also noticed that some terms were either highlighted green or red but when I hovered over them it didn’t tell me what the highlighted colors meant, I assumed they were either positive or negative. For example, “dead” or similar iterations of the word, “monster”, “darkness” and “stupid” were highlighted in red. Other terms like “nice”, “hero”, “love” and “magic” were highlighted green.

When doing this assignment, I was curious about how often certain phrases or words were used because of running jokes between each book’s fandom. For Percy Jackson and the Lightning Thief, if I select the top phrases for Annabeth Chase, one of the main characters, I get “Annabeth yelled” which could be because her life and the lives of her friends are usually in danger.

Secondly, I used an article titled Race and Gender by Gebru. The most frequently used words were more varied than in the books, which made sense since the books are narrated in the first person. Gender was used 53 times, and AI was used 50 times. Some of the terms highlighted in red were “issues,” “inequality,” “harassment,” “errors,” and “racist.” Terms like “powerful,” “ethical,” “success,” and “fair” were highlighted green.

It was also interesting to see the results of comparing two documents. Distinctive words between the two get added to the summary section and the trends graph compares how frequently specific terms appear in each document. For example, “archives” and “archival” appear more frequently in Toward slow archives but have fewer appearances in Johnson Markup bodies. On the other hand, in Johnson Markup bodies terms like “black” and “digital” had a higher frequency rate than Toward slow archives.

In general, I think the text analysis provides more informative analyses for academic articles/texts. I did like the overall interface of the tool. There were a lot of features to explore; there was a summary section that included vocab density, readability index, average words per sentence, and most frequent words in the document. Another section called “Reader” featured the document that could be read in real-time and yet another section included a graph that displayed word frequency trends at document segments.