PLEASE SOLVE THESE PROBLEMS IN PYTHON USING NLTK

Q1: Load a corpus (of txt files) of your choice containing at least 10 text files using: 1. File method 2. PlaintextCorpus Reader Q2: Pre-process the corpus loaded in step 1(apply normalization, tokenization, stopword removal, stemming) Q3: Convert the corpus into Bag-of-Words and tf-idf feature matrix using: (a) TfidfVectorizer()and CountVectorizer (b) Without using in-built functions Q4: Explore how we can access, pre-process and create feature vector for HTML texts? (Hint: explore BeautifulSoup package) Q1: Load a corpus (of txt files) of your choice containing at least 10 text files using: 1. File method 2. PlaintextCorpus Reader Q2: Pre-process the corpus loaded in step 1(apply normalization, tokenization, stopword removal, stemming) Q3: Convert the corpus into Bag-of-Words and tf-idf feature matrix using: (a) TfidfVectorizer()and CountVectorizer (b) Without using in-built functions Q4: Explore how we can access, pre-process and create feature vector for HTML texts? (Hint: explore BeautifulSoup package)