I have gone through numerous documents to read about doc2Vec and word2Vec. I do understand how powerful it is to represent the words as a vector and to perform simple operations like vector addition , subtraction to yield meaningful analogy between the words. Although one thing I am still not able to understand is how this technique can be used to understand user sentiments .
Can someone please elaborate as to how user sentiments are analysed using these techniques ? Thanks Samir
By representing a document or set of words with feature vectors, you can process text in other machine learning tasks. For example if you have a dataset which labeled each document x with its sentiment y, you can use the pretraind embedding as feature vectorisation to represent x as input to your machine learning method and test if these features help your task.