This section will give you an idea of which kinds of NLP applications use word2vec and how NLP applications use this concept. Apart from that, I will also discuss some of the most frequently-asked questions across the community in order for you to have a clear insight of word2vec when you try it out in real life.
NLP applications such as document classification, sentiment analysis, and so on can use word2vec techniques. Especially in document classification, word2vec implementation gives you more good results, as it preserves semantic similarity.
For sentiment analysis, we can apply word2vec, which gives you an idea about how words are spread across the dataset, and then you can use customized parameters such as context window size, subsampling, and so on. You should first generate bag of words (BOW) and then start to train word2vec...
