How to use different classes of words in CountVectorizer()
First of all your question is about stemming words as mentioned in the other answer which can be found in any Python NLP library such as Spacy or NLTK.

The other point to mention here is that despite the other answer, what libraries has as Stop Words list is not actually stop word! Do no remove them! In NLP stop words should be extracted based on working corpus not based on a predefined list. In practice removing this kind of stop words usually reduces the performance on specific domain corpuses.

The Third point is that depending on the classifier and loss function you use, TF-IDF might be better than Count Vectorizer. I suppose it works better specially if Log Loss is the cost function but I am not sure. Just give it a try.