Text classification using Word2Vec
One of the methods to perform text classification is to convert the words into embedding vectors so that you can use those vectors for classification. Word2Vec is a well-known method to perform this task.
Word2Vec
Word2Vec is a group of neural network-based models that are used to create word embeddings, which are dense vector representations of words in a continuous vector space. These embeddings capture the semantic meaning and relationships between words based on the context in which they appear in the text. Word2Vec has two main architectures. As mentioned previously, the two main architectures that were designed to learn word embeddings are CBOW and skip-gram. Both architectures are designed to learn word embeddings by predicting words based on their surrounding context:
