preclassified trained twitter comments for categorization
The thing is that how do I even generate/create a training data for
  such a huge data

I would suggest finding a training data set that could help you with the categories you are interested in. So let's say price related articles, you might want to find a training data set that is all about price related articles and then perhaps expand it by using synonyms for key-words like cheap or so. And perhaps look into sentence structure to find out whether if the structure of the sentence helps with your classifier algorithm.  

If not then what is the best approach to create a training data for
  multi-class classification of text/comments? key-words, pulling articles that are all about related categories and then go from there. 

Lastly, I suggest being very familiar with NLTK's corpus library, this might also help you with retrieving training data as well. 
As for your last question, I'm kinda confused to what you mean by 'multiple categories to classify the comments into', do you mean having multiple classifiers for a particular comment to belong in? So a comment can belong to 1 to more classifiers?
So I have some 1 million lines of twitter comments data in csv format. I need to classify them in certain categories like if somebody is talking about  : "product longevity", "cheap/costly", "on sale/discount" etc. 
As you can see I have multiple classes to classify these tweets data into.
The thing is that how do I even generate/create a training data for such a huge data.Silly question but I was wondering whether/not there are already preclassified/tagged comments data to train our model with? If not then what is the best approach to create a training data for multi-class classification of text/comments ?
While I have tried and tested NaiveBayes for sentiment classification for a smaller dataset, could you please suggest  which classifier shall I use for this problem (multiple categories to classify the comments into).
Thanks!!!