Text Mining of Tweets. Companies can learn a lot about customer experiences by monitoring the social media web site Twitter. The file AirlineTweets contains a sample of 36 tweets of an airline's customers. Normalize the terms by using stemming and generate frequency and binary document-term matrices.

a.) What are the five most common terms occurring in these tweets? How often does each term appear?

b.) Using Jaccard's distance to compute dissimilarity between observations, apply hierarchical clustering employing. Ward's linkage method to yield three clusters on the binary document-term matrix using the following tokes as variables: agentm attend, bag, damag, and rude. How many documents are in each clusters? Give a description of each cluster.

c.) How could managemnet use the results obtained in part (b)?