I used nltk to extract only nouns and to do word stemming (e.g. so that "time", "times" and "timing" are only counted as one word).
I also experimented a lot with various method of determining word size i.e. size proportional to frequency, size proportional to log(frequency), size proportional to sqrt(frequency).
It's funny, I did exactly the same, with my Hangouts Takeout extract, a couple of weeks ago, but didn't go as far, because I kept struggling with stopwords and some ways to filter out uninteresting stuff (my implementation was much more naive than yours). I'm still thinking about what other types of analysis I could perform on that interesting dataset though (because it's so personal after all).
This is a great tool! Ironically, I might use it someday to illustrate why word clouds are an absurd format. But it'll be great for joking around too :)
I wrote a paper about word clouds for my Information Retrieval class when I was in Library School, when word clouds were still a trendy and popular content browsing UI.
I concluded that they do have some utility as "semantic cartograms" in certain contexts, but beyond that they're mostly just decoration. Especially the ones created by this tool. =)
A while ago I tried to write some wordcloud code, but couldn't quite figure out how to do the layout. Does anyone know where one might find a good writeup of the algorithm that tends to be used for this?
I know what the word means, and I don't see how it could possibly make sense here. If he doesn't mean stylish, he might mean stylized.
What could "word clouds pertaining to style" possibly mean? Maybe if you gave it a text and it spit out a cloud with things like: "using passive voice," "flowery," "strident," "long-winded," "plain."
- https://raw.githubusercontent.com/brianquinlan/word-cloud-va...
- https://github.com/brianquinlan/word-cloud-valentine/blob/ma...
My implementation (https://github.com/brianquinlan/word-cloud-valentine) is a lot less sophisticated than stylecloud but I think that I had a few interesting ideas about text extraction.
I used nltk to extract only nouns and to do word stemming (e.g. so that "time", "times" and "timing" are only counted as one word).
I also experimented a lot with various method of determining word size i.e. size proportional to frequency, size proportional to log(frequency), size proportional to sqrt(frequency).
I concluded that they do have some utility as "semantic cartograms" in certain contexts, but beyond that they're mostly just decoration. Especially the ones created by this tool. =)
A while ago I tried to write some wordcloud code, but couldn't quite figure out how to do the layout. Does anyone know where one might find a good writeup of the algorithm that tends to be used for this?
[1] http://wordcram.org/
https://www.nickyreinert.de/wordcloud-in-powerpoint/
It does not cover white-space with words, as Wordle et al will do. Also it's in German. But I guess the code itself is quite clear.
What could "word clouds pertaining to style" possibly mean? Maybe if you gave it a text and it spit out a cloud with things like: "using passive voice," "flowery," "strident," "long-winded," "plain."