将客户的评论分为好、差和中立
我有一个典型的人工智能问题需要解决。客户将提交有关产品的评论。我必须能够创建一个程序，将这些评论分类为好、坏或中立。
当然，神经网络将在其中发挥重要作用。 另外，我认为模糊逻辑可以在其中发挥一些作用。比如评论的好坏或中立程度！ 关于如何解决它的更多想法？
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 logic 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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这个问题通常被称为情感分析。您可以查看有关情绪分析的维基百科条目进行简要回顾，或者刘冰的情感分析页面，获取更详细的资源和教程。
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.
您可以使用某种形式的监督学习。
分类最重要的是选择正确的特征。 “特征”意味着您从评论中提取一些值，这些值仍然抓住了分类任务的本质。我想到的是
然后您可以使用任何可用的机器学习算法（神经网络，SVM） ）并训练一个分类器，前提是您有足够多的带有好/中/差标签的评论。
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
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.
神经网络肯定会起作用，但是我对引入新单词和语言持怀疑态度。我会采用贝叶斯网络方法来确定处于“好/中/坏”状态的概率。在将数据放入贝叶斯网络之前，您应该考虑清理数据[词干提取等]。
另外：元属性 [ziggy 提到的] 更多的是提高您所采用方法的性能的指标。
编辑：贝叶斯网络是监督学习的一种形式。
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.