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pythonnlpword2vecdoc2vec

What should be used between Doc2Vec and Word2Vec when analyzing product reviews?

I collected some product reviews of a website from different users, and I'm trying to find similarities between products through the use of the embeddings of the words used by the users. I grouped each review per product, such that I can have different reviews succeeding one after the other in my dataframe (i.e: different authors for one product). Furthermore, I also already tokenized the reviews (and all other pre-processing methods). Below is a mock-up dataframe of what I'm having (the list of tokens per product is actually very high, as well as the number of products):

Product reviews_tokenized
XGame3000 absolutely amazing simulator feel inaccessible ...
Poliamo production value effect tend cover rather ...
Artemis absolutely fantastic possibly good oil ...
Ratoiin ability simulate emergency operator town ...

However, I'm not sure of what would be the most efficient between doc2Vec and Word2Vec. I would initially go for Doc2Vec, since it has the ability to find similarities by taking into account the paragraph/sentence, and find the topic of it (which I'd like to have, since I'm trying to cluster products by topics), but I'm a bit worry about the fact that the reviews are from different authors, and thus might bias the embeddings? Note that I'm quite new to NLP and embeddings, so some notions may escape me. Below is my code for Doc2Vec, which giving me a quite good silhouette score (~0.7).

product_doc = [TaggedDocument(doc.split(' '), [i]) for i, doc in enumerate(df.tokens)]
model3 = Doc2Vec(min_count=1, seed = SEED, ns_exponent = 0.5)
model3.build_vocab(product_doc)
model3.train(product_doc, total_examples=model3.corpus_count, epochs=model3.epochs)
product2vec = [model3.infer_vector((df['tokens'][i].split(' '))) for i in range(0,len(df['tokens']))]
dtv = np.array(product2vec)

What do you think would be the most efficient method to tackle this? If something is not clear enough, or else, please tell me.

Thank you for your help.

EDIT: Below is the clusters I'm obtaining: Clusters obtained

Solution

  • There's no way to tell which particular mix of methods will work best for a specific dataset and particular end-goal: you really have to try them against each other, in your own reusable pipeline for scoring them against your desired results.

    It looks like you've already stripped the documents down to keywords rather than original natural text, which could hurt with these algorithms - you may want to try it both ways.

    Depending on the size & format of your texts, you may also want to look at doing "Word Mover's Distance" (WMD) comparisons between sentences (or other small logical chunks of your data). Some work has demo'd interesting results in finding "similar concerns" (even with different wording) in the review domain, eg: https://tech.opentable.com/2015/08/11/navigating-themes-in-restaurant-reviews-with-word-movers-distance/

    Note, though, WMD gets quite costly to calculate in bulk with larger texts.