Hi, I&#x27;m the author. I appreciate the time you&#x27;ve taken to read and provide constructive criticism of my work. Here&#x27;s my full write up (on GitHub, so it should continue to work): <a href="https:&#x2F;&#x2F;github.com&#x2F;walkerkq&#x2F;textmining_southpark&#x2F;blob&#x2F;master&#x2F;southpark.pdf" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;walkerkq&#x2F;textmining_southpark&#x2F;blob&#x2F;master...</a><p>I was working under the assumption that we do not know ALL the words since the show&#x27;s been renewed through 2019. This covers the first 18 seasons.<p>Additionally, the counting up their most frequent words produced results with very little semantic meaning - things like &quot;just&quot; and &quot;dont&quot; - which can be seen in this (really boring) wordcloud: <a href="https:&#x2F;&#x2F;github.com&#x2F;walkerkq&#x2F;textmining_southpark&#x2F;blob&#x2F;master&#x2F;plots&#x2F;southpark_wordcloud.png" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;walkerkq&#x2F;textmining_southpark&#x2F;blob&#x2F;master...</a><p>Looking into the log likelihood of each word for each speaker produced results that were much more intuitive and carried more meaning, like ppod said below: <i>I think the idea is that what we are really trying to measure is something unobservable like the underlying nature of the character or the writers&#x27; tendencies to give characters certain ways of speaking.</i>