You work for the security department of your firm. As part of securing your firm’s email activity from phishing attacks, you need to build a machine learning model that analyzes incoming email text to find word phrases like “you’re a winner” or “click here now” to find potential phishing emails. 
Which of the following text feature engineering techniques is the best solution for this task?

Answer options:

A.Orthogonal Sparse Bigram (OSB)
B.Term Frequency-Inverse Document Frequency (tf-idf)
C.Bag-of-Words
D.N-Gram

Answer: D
Option A is incorrect. The Orthogonal Sparse Bigram natural language processing algorithm creates groups of words and outputs the pairs of words that include the first word. You are trying to classify an email as a phishing attack by having your model learn based on the presence of multi-word phrases in the email text, not pairs of words from the email text stream using the first word as the key.
Option B is incorrect. Term Frequency-Inverse Document Frequency determines how important a word is in a document by giving weights to words that are common and less common in the document. You are trying to classify an email as a phishing attack by having your model learn based on the presence of multi-word phrases in the email text. You are not trying to determine the importance of a word or phrase in the email text.
Option C is incorrect. The Bag-of-Words natural language processing algorithm creates tokens of the input document text and outputs a statistical depiction of the text. The statistical depiction, such as a histogram, shows the count of each word in the document. You are trying to classify an email as a phishing attack by having your model learn based on the presence of multi-word phrases in the email text, not individual words.
Option D is correct. The N-Gram natural language processing algorithm is used to find multi-word phrases in the text, in this case, an email. This suits your phishing detection task since you are trying to classify an email as a phishing attack by having your model learn based on the presence of multi-word phrases.
Reference:
Please see the article titled Introduction to Natural Language Processing for Text, and the article titled Document Classification Part 2: Text Processing (N-Gram Model & TF-IDF Model)