# Extract key phrases for binary outcome

I have a set of phrases that lead to a binary outcome (accept/reject) and I was wondering what techniques are most helpful for extracting key phrases that are most likely to determine the outcome, given that I have a training set of data that has the English-language phrase and the observed outcome.

To illustrate the idea let me give a simple example:

## Accepted

• Sounds great
• That would be great
• That's fine

key words: great, fine

## Rejected

• I'm not sure
• I don't think so
• No way

key words: not, don't, no

• Counterpoint: "Not bad at all!" "Great god, this is awful." – Sleepy Miles Mar 13 at 7:08
• Very good point, I have run into similar such issues before. I think N-gram analysis can help this, perhaps starting at 3-grams for instance so that smaller phrases don't create conflicting scenarios. – z73758437095439 Mar 13 at 16:59

If your goal is to gain insight into the phrases that are being used in each group, then I'd recommend looking for the most frequent N-grams of different lengths that appear in each class. Here is a related stackoverflow question that shows how you can use nltk and sklearn to extract these.