CountVectorizer is a great tool provided by the scikit-learn library in Python. It is used to transform a given text into a vector on the basis of the frequency (count) of each word that occurs in the entire text. This is helpful when we have multiple such texts, and we wish to convert each word in each text into vectors (for further text analysis). Considering the the following sample texts/ reviews (have been collected from online review), show your work, how CQuntVecorizer works and generates a corresponding vector matrix. reviews = ["We like our university", "Students are good", "Good students and faculties", "Staff was rude", "Rude staff and not good"] [Note: GountVectorizer Plain and Simple: uses utf-8 encoding. Performs tokenization (converts raw text to smaller units of text) uses word level tokenization (meaning each word is treated as a separate token) ignores single characters during tokenization (say goodbye to words like ' a ' and 'I']