TF-IDF knows dog and puppy are different words. It does not know they mean nearly the same thing. A classifier trained on dog cannot generalize to a review about puppy. You can paper over this by listing synonyms, but that fails on rare terms, domain jargon, and every language you did not anticipate.
You want a representation where dog and puppy land close together in space. Where king - man + woman lands near queen. Where a model trained on dog transfers some signal to puppy for free.
Word2Vec gave us that space. Two layer neural network, trillion-token training runs, published in 2013. The architecture is almost embarrassingly simple. The results reshaped NLP for a decade.
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