In this question, you need to complete the tasks listed below. For these tasks, use "Health News in Twitter dataset" from UC Irvine Machine Learning Repository$\mathrm{.}2$

$1.$ Load the dataset in a pandas DataFrame; use only foxnewshealth.txt and cnnhealth.txt for next tasks. Use the tweets column only, for further tasks.

$2.$ Using regular expressions, extract hashtags in the tweets and store them. Keep a counter for each hashtag.

$3.$ Then sentence tokenize each tweet and then word tokenize each sentence. Keep a counter of each word and sort them in descending order. Notice that there are a lot of special characters and repeated words with different cases, like 'New' vs 'new'.

$4.$ Clean the tweets by removing the special characters and unwanted strings like numbers, HTTP links, etc. Use regular expressions to do so$.$ Normalize the case of all the characters in all the tweets.

$5.$ Remove Stopwords from all the text examples. You may use any library like SpaCy or NLTK$.$ Repeat task $3$ and notice the difference.

$6.$ Lemmatize the words in step $5$ to their base form using either NLTK or SpaCy. Use stemming to the same words in $5$ and observe the difference.

$7.$ Maintain a corpus of all the words that appeared in the given files.