• Term Frequency-Inverse Document Frequency TF-IDF: is calculated for each term in the document by combining TF and IDF as shown in the following equation 3: Word Embeddings "word2vec": Word embeddings are based on a probabilistic feed forward neural network language model to learn a space of continuous word representations in which similar words are expected to be close to each other.
word embeddings allows representing words with low dimensional and dense real-value vectors which capture useful semantic and syntactic features of words (Law and al, 2017), on the one hand, and reducing computational complexity, on the other hand.