8.2 Clustering the Users Who Posted Tweets About the Public Figure

We want to categorize (cluster) the users of the tweets about the Public Figure based on the descriptions provided in their Twitter account. Descriptions in the users' Twitter profiles give a

short piece of information about the Twitter handle. To cluster users, build a document term matrix by using the user descriptions of the tweets (note, not tweets themselves) you downloaded in section 8.1.

1. Use rtweet packages users_data(tweets) function to extract users data from tweets data object you downloaded in Section 8.1. Store the unique author result in variable, authors.

2. Clean the data by pre-processing and then create a weighted Term Document Matrix using unique users descriptions.

3. Compute the appropriate number of clusters using the elbow method. Use cosine distance.

4. Cluster the users and visualize the clusters in two dimensional vector space.

5. Display the count of users in each cluster

6. List a maximum of 10 screen names of users in each cluster.

7. List the top 10 words in each cluster

8. Display the description of the first five users in each cluster.

9. Comment on your findings.