Feature engineering techniques:

• Bag of Words Model (TF)
• Bag of N-grams Model
• TF-IDF Model

There are some potential problems which might arise with the Bag of Words model when it is used on large corpora. Since the feature vectors are based on absolute term frequencies, there might be some terms which occur frequently across all documents and these may tend to overshadow other terms in the feature set. The TF-IDF model tries to combat this issue by using a scaling or normalizing factor in its computation. TF-IDF stands for Term Frequency-Inverse Document Frequency, which uses a combination of two metrics in its computation, namely: term frequency (tf) and inverse document frequency (idf). This technique was developed for ranking results for queries in search engines and now it is an indispensable model in the world of information retrieval and NLP.

Mathematically, we can define TF-IDF as tfidf = tf x idf, which can be expanded further to be represented as follows.

Here, tfidf(w, D) is the TF-IDF score for word w in document D.

• The term tf(w, D) represents the term frequency of the word w in document D, which can be obtained from the Bag of Words model.
• The term idf(w, D) is the inverse document frequency for the term w, which can be computed as the log transform of the total number of documents in the corpus C divided by the document frequency of the word w, which is basically the frequency of documents in the corpus where the word w occurs.
• Similarity Features
• Clustering using Document Similarity Features

• Word2Vec
• GloVe
• FastText

• Google Word2vec

• BERT ( Similarity & Representantion)

• Word2Vec

• GloVe

• FastText