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First, looking back at the blog posts from last week, I really enjoyed reading the blog titled: “Analysis May be Computerized, but Meaning Remains Human.”  The author, in my opinion, correctly noted that although computers performed most of the work, there is still an element to this analysis that is very human.  We still get to discern the results we get from algorithms.  Point being, the results we get from algorithms are not the end all be all.  It is still up to humans to interpret the results in a responsible manner.

With that said, I really enjoyed the reading for this week.  It was very clear and concise, and really got to, what I think, is the crux of the issue for topic modeling.  This reading highlighted the idea that it is very easy to see how topic modeling and either think that this is a powerful tool and we should use it as much as possible.  On the contrary, it is also very easy to see topic modeling and immediately disregard it.  After all, how could a computer algorithm give any real meaning to written text.  This article pushes back on both schools of thought, and notes the advantages that come from topic modeling lie in the middle of these two schools.  Meaning, topic modeling adds an additional data point, and it is up to human to decide what weight to put on the analysis.  As the authors of the reading say, topic modeling is simply a tool in a shed, and it is up to the researcher to interpret and weight the results.