Topic Modeling – Sherlock Holmes

This week’s assignment was actually really fun, I enjoyed experimenting different combinations of Topic Modeling and comparing the results, but the best part for sure was trying to unveil what possible labels I could use for each group of words. When I chose a combination of settings with more than 20 words printed it got tough to label them, I always had the feeling that the label I chose wasn’t good enough for most of the words. Also it is much better to chose less words in the same topic so that it looks more organized and better specified -you can just “jump in” to the part of the story or situation you want-.

When I first played with Malet I used the default settings but I changed the number of interations from 200 to 500, with10 words printed and 10 topics, it was very easy to label them once I had to work with only 10 words in the same topic, and as I said, when you have less words to group you can be even more specific.

For my second attempt I decided to do something a little different and contrasting to what I found to be easier, so I changed the settings to 30 topics, 2000 interations and 50 words printed. Worst decision ever! I had to look through at least 10 topics until I found one that I could relate all the words.

In my last time playing with the tool I decided to use all the settings that I liked better, so I chose 30 topics, 2000 interations and 10 words printed, the results were amazing and really easy to work with, also, the most interesting thing about this attempt is that all my labels were related in a certain way with crime and mystery.


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