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I ran a survey asking this question: 'What do you like the most about your favorite character in the game?' and I got 23K responses; most of them are phrases/sentences; what's the best way to analyze these results such as to find out what the main responses are? A simple word cloud may not do because it strips out the context by isolating words; but I'm open. Would love help. Didn't know how to attached the dataset (my first time on Stack-Ex) so [here][1] it is. Thanks in advance fellow coders/analysts.

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  • $\begingroup$ Check out Latent Dirichlet Allocation (LDA). It's a probabilistic model for words that sorts words and responses into a pre-determined number of topics. $\endgroup$
    – TBSRounder
    Mar 23, 2016 at 19:48
  • $\begingroup$ Sentiment Analysis might be another option, check out the syuzhet package in R. $\endgroup$
    – DaBenski
    Mar 23, 2016 at 21:08
  • $\begingroup$ you can add the link as [link](http://example.com), what is your base point to decide the main responses are ?, do you want classification of the sentences into positive and negative sentence? $\endgroup$ Apr 23, 2016 at 3:34
  • $\begingroup$ you can always compute n-grams for your corpus and determine their frequency so that they can be displayed in a word cloud (or more accurately a phrase cloud). $\endgroup$ Jun 21, 2016 at 23:07
  • $\begingroup$ Consider the discussion to this question: datascience.stackexchange.com/questions/9886/… $\endgroup$
    – mapto
    Oct 29, 2018 at 8:16

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