diseases risk prediction using social media comments
You should also have comments/symptoms of a reference group of non-diabetes patients to be able to make a distinction yes/no diabetes. 

Do you have users of which you know they don't have diabetes?
If not, "Is often thirsty?" will always map to diabetes, because you have no other data. 

Non-realistic example: 


Suppose 99% of diabetes patients eat 10 candy bars a day, and 1% does not eat candy bars.
Then suppose Suppose 99% of non-diabetes patients eat 10 candy bars a day, and 1% does not eat candy bars.
There is a strong correlation between eating candy bars and having diabetes.
Your data of only diabetes patients shows that both people who eat 10 candy bars and people who eat no candy bars develop diabetes.
Data that includes non-diabetes patients will reveal that people that eat 0 candy bars are much more likely to not have diabetes.


Or you should have comments and diabetes type data to be able to make a distinction diabetes type 1 or type 2.
"How often do you use medication?" is a strong indication between the two, but you'd need the data.

Furthermore, expect a risk score with at least weighted numbers that can be either positive or negative, for weak or strong (inverse) correlation between an answer and indication for diabetes.

There are a lot more advanced techniques (e.g. with neural networks) that will have better results, including finding correlations between answers.

If you are doing anything more serious than 'Go see the doctor' on >50% of positive indicators, you should really do more research on data science models, but also on how to formulate the questions etc.