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 CountVecorizer 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: CountVectorizer 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 $\text{'}$a$\text{'}$ and $\text{'}$I$\text{'}]$