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Something that really stuck out to me in Meeks and Weingart’s article “The Digital Humanities Contribution to Topic Modeling” was the difficulties that are highlighted when using topic modeling. The first point that drew my attention was the idea that “what we might have identified as cohesive ‘topics’ are more complex than simple thematic connections”. It makes sense that people may assume topics they extract from modeling represent cohesive ideas and themes throughout the piece of literature, but the reality is discerning a topic can be very broad. Topics lead into subtopics, and those subtopics have their own subtopics. The reality is this modeling may give the umbrella topic of a piece, but can often lack specificity of subjects addressed within the larger topic. This leads into the next point I found interesting; that “different methodological choices may lead to contrasting results”. People will use different code and methods to try to extract topics, and the even slight differences could lead to varying conclusions about the text. This creates misconceptions about the text, which topic model is more representative? Which is more validated and why? These questions are all debatable, which creates vagueness in any conclusion drawn about a topic model.

 

In response to my colleague CD, the line that “no amount of counting can produce meaning” also resonated with me. They make great points in the article about the uses of topic modeling and how it can be incredibly effective at certain things, but the reality remains that, as you said, “the power of interpretation and meaning still lies in the hands of the individual”. Great point, I agree fully!