What is a good approach for extracting keywords from user-submitted text?

I'm building a site that allows users to make sense of a debate by graphically representing arguments for and against a particular issue. (Wrangl)

I'd like to categorise these debates so they are more easily found and connected. I don't want to irritate the person creating the debate by asking them to add tags and categories before they see any benefit, so I'm looking at a way of automatically extracting keywords.

What's a good approach for taking the debate's title and description (and possibly the content of the argumen开发者_如何学Pythonts themselves once there are some) to pull out, say, ten strong keywords that could be used as metadata to connect similar debates together, or even as the content of the "meta" keywords tag in the head of the HTML page where the debate is viewable. Eg. Datamapper vs ActiveRecord

The site is coded in Ruby with Sinatra, using DataMapper for data storage. I'm ideally looking for something which will work on Heroku (I don't have a way of writing files to disk dynamically), and I'd consider a web service, an API or ideally a Ruby gem.

Maybe you can use TextAnalyzer.

I understand that you're wanting to find an easy way of achieving this, I've recently dived into the world of NLP (Natural Language Processing) and Text-mining and its a daunting process of which most went far above my head.

Although i managed to code some functionality that resembles what you're looking for, though I did it in PHP. What i would suggest, that if you want it tailored to your project (Wrangl) then do it yourself.

Using the Porter stemming algorithm which I'm sure there will be Ruby code for. Ruby Porter stemmer

You can try the salsaAPI to automatically extract keywords and categorize the debates!