NLP: any easy and good methods to find semantic si

Question

I don't know whether StackOverflow covers NLP, so I am gonna give this a shot. I am interested to find the semantic relatedness of two words from a specific domain, i.e. "image quality" and "noise". I am doing some research to determine if reviews of cameras are positive or negative for a particular attribute of the camera. (like image quality in each one of the reviews).

However, not everybody uses the exact same wording "image quality" in the posts, so I am out to see if there is a way for me to build something like that:

"image quality" which includes ("noise", "color", "sharpness", etc etc) so I can wrap all everything within one big umbrella.

I am doing this for another language, so Wordnet is not necessarily helpful. And no, I do not work for Google or Microsoft so I do not have data from people's clicking behaviour as input data either.

However, I do have a lot of text, pos-tagged, segmented etc.

Answer 1

You might want to take a look at the book Opinion mining and sentiment analysis. If you are only interested in similarity of words and phrases, this survey paper may help you: From Frequency to Meaning: Vector Space Models of Semantics