How to solve most NLP problems: a step-by-step guide
Word2Vec and bag-of-words/tf-idf are somewhat obsolete in 2018 for modeling. For classification tasks, fasttext (<a href="https://github.com/facebookresearch/fastText" rel="nofollow">https://github.com/facebookresearch/fastText</a>) performs better and faster.<p>Fasttext is also available in the popular NLP Python library gensim, with a good demo notebook: <a href="https://radimrehurek.com/gensim/models/fasttext.html" rel="nofollow">https://radimrehurek.com/gensim/models/fasttext.html</a><p>And of course, if you have a GPU, recurrent neural networks (or other deep learning architectures) are the endgame for the remaining 10% of problems (a good example is SpaCy's DL implementation: <a href="https://spacy.io/" rel="nofollow">https://spacy.io/</a>). Or use those libraries to incorporate fasttext for text encoding, which has worked well in my use cases.