Classification of review from customers into Good,Bad and Neutral

I have a typical AI Problem to solve. Customers gonna submit comments about a product. I have to be able to create a program that classify these comment as either good,bad or neutral.

Surely, Neural Network gonna play a great role in it. Also, I think fuzzy log开发者_StackOverflow社区ic can play some role in it. Such as how far a comment is good,bad or neutral!! Some more ideas about how to solve it??

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This problem is usually referred to as Sentiment Analysis. You can check out the wikipedia entry about Sentiment Analysis for a brief review, or Liu Bing's page on sentiment analysis for more detailed resources and tutorials.

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You can use some form of supervised learning.

The most important thing for classification is then choosing the right features. "Features" means you extract some values from the review that still capture the essence with respect to the classification task. Things that come to my mind are

• number of words
• average number of words per sentence
• number of words from some set like {crap, shit, damn, viagra, ...}

Then you can use any available machine learning algorithm (neural networks, SVM) and train a classifier given you have enough reviews that are labeled with good/neutral/bad.

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Neural networks would certainly work for it, however I would be supicious about introducing new words, and languages. I would go for a Bayes net approach for determining the probability of being in a "good/neutral/bad" state. You should consider cleaning the data [stemming, etc] before putting it through the bayes net.

Additionally: The meta attributes [what ziggy mentioned] are more of an indicator to boost the performance of the approach you take.

EDIT: Bayes-Nets are a form of supervised learning.