Part 2: Text Mining

A dataset of Shark Tank episodes is made available. It contains 495 entrepreneurs making their pitch to the VC sharks.

You will ONLY use “Description” column for the initial text mining exercise.

1. Pick out the Deal (Dependent Variable) and Description columns into a separate data frame.
2. Create two corpora, one with those who secured a Deal, the other with those who did not secure a deal.
3. The following exercise is to be done for both the corpora:

• Find the number of characters for both the corpuses.
• Remove Stop Words from the corpora. (Words like ‘also’, ‘made’, ‘makes’, ‘like’, ‘this’, ‘even’ and ‘company’ are to be removed)
• What were the top 3 most frequently occurring words in both corpuses (after removing stop words)?
• Plot the Word Cloud for both the corpora.

4. Refer to both the word clouds. What do you infer?
5. Looking at the word clouds, is it true that the entrepreneurs who introduced devices are less likely to secure a deal based on your analysis?