Analysis of Customer satisfaction surveys I have customer feedback data about 2-3 products from 100 customers. Number of questions are around 160. I have data in excel format. Header row contains the question and row below contains the responses from customers. Not all questions have been answered by each customer.  I have to categorize the feedback as either positive or negative. The analysis needs to be done in R. 
This data contains boolean responses, numerical responses and text responses. 
I've collected the data for text responses in a separate sheet and trying to do sentiment analysis using sentiment analysis package and the general text mining package in R. 
How to do the sentiment analysis of boolean and numerical responses ?
Also, Can I combine the score from all the above three analysis and can categorize the response as positive or negative overall ?
 A: This might be the wrong answer, but I work daily with such datasets in market research. Standard practice looks like this:


*

*Identify variables which could define differences (product, gender, age etc. are obvious candidates)

*Identify variables who are of interest, e.g. overall satisfaction. If the variables have a meaningful scale like a 6 point scale recodethem into top 2 / bottom 2 values - the middle values are often of no intereset.

*Produces crosstables with the variables from #1 in the columns, and #2 as rows.
The advantage of this method is that missing values etc. are not fatal - they reduce the number of cases in some table, but not in all. Disdvantage is the low statistical power of such tests.

*If there are text responses, survey the responses (10%) and generate some sort of key like "satisfied customer", "buttons are to small" etc. pp. - then create 0/1 variables for each key-element. Then some poor bastard has to code each response into the key(s). Boring, but doable.

*Advanced analysis like regression / correlation or what else seems apropriate.
