Can you explain each method Word2Vec, Skip-gram, GloVe? How these methods are useful?
Out of three which method is more appropriate for giving higher results in text classification?
You may take a look at these manuscripts to understand these models.
https://arxiv.org/pdf/1301.3781v3.pdf
https://aclanthology.org/D14-1162.pdf
It's difficult to say which one performed well for text classification. I think it entirely depends on your dataset. Better explore each method and analyze which one performs well.
this link may help you
https://analyticsindiamag.com/word2vec-vs-glove-a-comparative-guide-to-word-embedding-techniques/
All mentioned methods are used for words, not for texts. Of course, you can calculate the sum of the individual word vectors in a text and then use a classifier.
Depending on the type of text and classification task, you might not need word embeddings (use TF/IDF) or get better results with finetuning a contextualized BERT model.
this link could help you
https://towardsdatascience.com/introduction-to-word-embedding-and-word2vec-652d0c2060fa
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