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I have a dataset of user written reviews. What I would like to do is to cluster similar reviews together.

I have already trained a word2vec model on vocabulary of 50000 words, and I have updated my data frame so that each user review has dimensions (numb_of_words, 375) where numb_of_words indicates a number of words in a given user review and 375 indicates the length of word embedding.

My question is this, can I use K-Means to cluster similar user reviews? Not individual words, but the whole reviews. If so, must I first get them all to the same dimension? Or must I perform some sort of dim. reduction technique first?

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The usual approach with word2vec seems to be to take the average vector of each review.

Then cluster these.

Good luck - I am all but convinced that this can work particularly well, because of the underlying assumption that every point is part of a cluster in the first place... But I guess many users don't care, they just need some quantifiable result for the hype cycle.

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