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    <p>Ahh... but "I really love dogs" and "I really hate dogs" are totally similar ;), both discuss one's feelings towards dogs. It seems that you're missing a step in there: </p> <ol> <li>Run your algorithm and get the general topic groups (i.e. "feelings towards dogs").</li> <li>Run your algorithm again, but this time on each previously "discovered" group and let your algorithm further classify them into subgroups (i.e. "i hate dogs"/"i love dogs").</li> </ol> <p>If your algorithm adjusts itself based on its experience (i.e. there some learning involved)., then make sure you run separate instances of the algorithm for the first classification, and a new instance of the algorithm for each sub-classification... if you don't, you may end up with a case where you find some groups and any time you run your algo on the same groups the results are nearly identical and/or nothing has changed at all.</p> <h2>Update</h2> <p><a href="http://mahout.apache.org/" rel="nofollow noreferrer">Apache Mahout</a> provides a lot of useful algorithms and <a href="https://cwiki.apache.org/confluence/display/MAHOUT/Quickstart" rel="nofollow noreferrer">examples</a> of Clustering, Classification, Genetic Programming, Decision Forest, Recommendation Mining. Here are a some of the text classification examples from mahout:</p> <ul> <li><a href="https://cwiki.apache.org/confluence/display/MAHOUT/Wikipedia+Bayes+Example" rel="nofollow noreferrer">Wikipedia classification</a> </li> <li><a href="https://cwiki.apache.org/confluence/display/MAHOUT/Twenty+Newsgroups" rel="nofollow noreferrer">Twenty Newsgroups classification</a></li> <li><a href="https://cwiki.apache.org/MAHOUT/creating-vectors-from-text.html" rel="nofollow noreferrer">Creating Vectors from Text</a></li> <li><a href="http://www.slideshare.net/gsingers/intro-to-apache-mahout" rel="nofollow noreferrer">Document Similarity with Mahout</a></li> <li><a href="http://mahout.apache.org/javadoc/core/org/apache/mahout/cf/taste/impl/recommender/GenericItemBasedRecommender.html" rel="nofollow noreferrer">Item Based Recommender</a> </li> </ul> <p>I'm not sure which one would best apply to your problem, but maybe if you look them over you'll figure out which one is the most suitable for your specific application.</p>
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    1. POalgorithm to calculate similarity between texts
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    1. COThey can be even more similar. For example, what if I say I love dogs, but mean it sarcastically? It is impossible to understand the exact meaning and sentiment of text posted online, because there is so much important information exposed through tone and diction.
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      1. USDan Joseph
    2. COYou are right. I've seem some guys using Self-Organizing Map for spam detection, maybe it would be a good candidate, thoughts?
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    3. CO@Daniel, given a single post, I don't think that even a human will be able to determine if it's sarcastic or if it's genuine. Sarcasm only works in context, so yes, if it's sarcastic then it will be a false classification.
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