I tried the regex stemmer, but I get hundreds of unrelated tokens. I'm just interested in the "play" stem. Here is the code I'm working with: import nltk from nltk.book import * f = open('tupac_or...

I am trying to do some very basic text analysis with the tm package and get some tf-idf scores; I'm running OS X (though I've tried this on Debian Squeeze with the same result); I've got a director...

I'm the "programmer" of a team of pupils that aims to investigate satisfaction and general problems in my grammar school. We have a questionary that is built upon a scale from 1-6 and we interpret ...

Possible Duplicate: Text Classification into Categories I am currently working on a solution to get the type of food served in a database with 10k restaurants based on their descripti...

I have already asked a similar question earlier but I have notcied that I have big constrain: I am working on small text sets suchs as user Tweets to generate tags(keywords). And it seems like the...

I'm trying to develop a python algorithm to check if a string could be an abbrevation for another word. For example fck is a match for fc kopenhavn because it matches the first characters of the ...

It should behave like Delicious toolbar for Firefox does; it lists possible tags to click. The effect is shown as below: The code should be able to find key words for the text. Any good algorit...

I'm looking for a java driven solution to a requirement for analysing sentences to log whether a key word was used positively or negatively. Ie The key word might be 'cabbages' and the sentence:-...

I have a series of text items- raw HTML from a MySQL database. I want to find the most common phrases in these entries (not the single most common phrase, and ideally, not enforcing word-for-word m...

I'm working on a project at the moment where I need to pick out the most common phrases in a huge body of text. For example say we have three sentences like the following: The dog jumped over the...

I need your help in determining the best approach for analyzing industry-specific sentences (i.e. movie reviews) for "positive" vs "negative". I've seen libraries such as OpenNLP before, but it's t...