Description of Data: This is the Tweet Sentiment dataset to predict the sentiment of tweets each record consists of the following attributes: The column or features in the dataset: $-$ textID $-$ unique ID for each piece of text $-$ text $-$ the text of the tweet $-$ sentiment $-$ the general sentiment of the tweet $-$ selected$\_$text $-[$train only$]$ the text that supports the tweet's sentiment $1.$ Preprocessed text should be included in the tweet data frame as cleaned$\_$text. Plot word cloud for all classes the tweets. $(1$ Marks$)2.$ Create two objects $\backslash ($ X $\backslash )$ and $\backslash ($ y $\backslash ).$ X will be the 'cleaned$\_$text $\text{'}$ column of tweet data frame and y will be the $\text{'}$ Sentiment' column. $(5$ Marks$)1.$ Create a BoW object and split the data into training and testing sets. Train a Decision tree model and Display the confusion Matrix. $2.$ Create a Bow object and split the data into training and testing sets. Train a MultinomialNB and decision tree model. Display the confusion Matrix for both train and test. $3.$ Compare the result of Decision tree and MultinomialNB