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Instructions
Customer Care professionals capture customer feedback and comments during their interaction with various customers everyday. This comments data in the form of text is referred to as unstructured data. There has been an exponential growth in unstructured data, and analyzing this data is critical for forming business strategies.
You have access to this Comments data. You need to build a text mining system to mine this textual data and (eventually) categorize each comment as Positive or Negative so that the business can effectively track customer sentiment and make appropriate corrective strategies.
For example, the first comment mentioned below should be categorized as Positive, and second one as Negative: –
The kind of offers and the kind of treatment you get of being card holder and kind of service and support you receive is great.
The card service is neither widely available nor acceptable compared to other card.
Download the attached dataset or use link.
Perform the following text cleaning techniques in R, and show your work:
Read through the following tutorial regarding some of the simplest ways of cleaning text data.
Analyze the data file and come up with what you might consider to be “stop words”. Are there any additional words you might want to exclude beside the standard ones? Discuss your findings.
Read the data into R, and provide the first few rows of data (hint: use the head function)
Do standard transformations (e.g., converting to plain text document, removing punctuation, and so on). Detail what you do with screenshots.
Stem the document for future use (we will use this data in future weeks)
Please copy/paste screen images of your work in R, and put into a Word document for submission. Be sure to provide narrative of your answers (i.e., do not just copy/paste your answers without providing some explanation of what you did or your findings).
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