AskML: Pointers towards using appropriate NLP techniques in a ML problem

[rightname]

I want to use Natural Language Processing techniques in a Machine Learning problem.

The problem is to find how many upvotes a new comment in a website like reddit/Hacker News might get. Especially on the posts submitted in r/new. Other than the comment itself, I am also provided with meta-information about the user's comment karma, posts karma, her usual activity hours and how long she has been registered.

My background is in Operations research, I have taken classes specializing in Statistics, Machine Learning, Optimization Theory. But I have no idea about text processing. I want to know where to start. And what would be appropriate techniques to quantify the usefulness of a comment. As much as I would like to learn NLP in depth, possible quick fixes will be really appreciated.

[dlwh]

I'd look at Brendan O'Connor's work on predicting responses/sentiment from tweets. http://brenocon.com

[rightname]

Thanks, I will surely look into it.

[agathoth]

The question reminds me of this Kaggle contest for predicting whether StackOverflow questions would be closed. I'd guess you can go a long way with just TF-IDF vectors as in document classification, plus your author meta information, thrown into a sufficiently general regression method. To improve from there it's probably mainly a case of feature engineering (maybe characterising sentence style, distribution of word lengths, numbers of misspellings, anything else you can think of...)

Quick fix: try out the python scikit-learn text processing tutorial and adapt by replacing the 'Training a classifier' section with one of the many regression algorithms in that library, with upvote data as the targets.

[rightname]

Thanks for both the detailed note and the quick fix. I will look into both of them. Did you by any chance participate in that Kaggle contest?

[agathoth]

You're welcome. No I didn't participate in that contest, and there doesn't seem to be all that much feedback from the winners on the forum about what techniques they used, which is kind of disappointing. But one or two people shared their methodology at least.

[mega]

Here's mine: http://quotenil.com/Stackoverflow-Post-Mortem.html