[adimitrov]

Sounds like a job for Sentiment Analysis [1]. Modern systems are pretty good at discerning negative from positive comments.

You could probably find a way to mark negative and positive comments. Whether the resulting algorithm would be fine-grained enough to semi-reliably mark 'middlebrow dismissal,' I really don't know. Actually, as somebody who has worked on that stuff in the past, I don't think it would be very easy.

[1] http://en.wikipedia.org/wiki/Sentiment_analysis

[pmelendez]

Thanks for the link.

Agreed I don't think it would be an easy task, but I wonder how would perform a "bag of word" approach.

Harder part as I see it would be to categorize the comments on middlebrow dismissal / Not dismissal. It seems like we would be spending more time preparing the data than in the algorithm itself.

[disgruntledphd2]

Welcome to statistics/data science/machine learning.

The hardest part is always getting the data into usable form. Its not as much fun as fitting models, but its definitely the majority of any role where people pay you to do this kind of stuff.

There's a lot of good research on forums (pm me if you want a bibliography i collected for a previous role), and short texts have become a bigger deal post Twitter. I completely agree with pg on the somewhat annoying nature of comments such as the GP.

[pmelendez]

Hey... Thanks for the offer! How can I contact you? I didn't know we can pm users over here.

I did have worked with ML before but mostly with images which are (IMHO) way easier to put in a format depending on the problem.

[disgruntledphd2]

My email is obsfuscated in my profile. Should be pretty clear, conditional on your humanity.

[benvanderbeek]

Bing Liu is one of the researchers working on this. I've discussed Amazon review fraud detection with him. http://www.cs.uic.edu/~liub/

[hntester123]

Hadn't heard of it; thanks for the link.