from sklearn.feature_extraction.text import CountVectorizer corpus = ["This is a sample sentence", "This is another sample sentence", "One more sample sentence"] # initialize CountVectorizer object count_vectorizer = CountVectorizer(analyzer='word', stop_words='english') # build the analyzer analyzer = count_vectorizer.build_analyzer() # apply the analyzer to a sentence print(analyzer('This is another sample sentence')) # output: ['sample', 'sentence'] # apply the analyzer to the whole corpus processed_corpus = [analyzer(sentence) for sentence in corpus] print(processed_corpus) # output: [['sample', 'sentence'], ['sample', 'sentence'], ['sample', 'sentence']] # create the count matrix count_matrix = count_vectorizer.fit_transform(corpus) print(count_matrix.toarray()) # output: [[1 1 1 1 0] # [1 1 1 0 1] # [0 0 1 1 1]]In the above code, we created a corpus consisting of three sample sentences. We then initialized a CountVectorizer object and built an analyzer from it. We applied this analyzer to a single sentence and the whole corpus to create a processed version of the corpus. Finally, we used the CountVectorizer object to create a count matrix that represents the presence of each word in each sentence.