算法，以确定如何积极或消极的声明/文字 [英] Algorithm to determine how positive or negative a statement/text is

问题描述

我需要一个算法来确定一个句子，段落或文章是消极或积极的语气......或者更好的是，如何正面或负面的。

例如：

  

    

杰森是最糟糕的SO用户，我曾经亲眼目睹（-10）

         

贾森是一个SO用户（0）

         

杰森是最好的SO用户我所见过的（+10）

         

杰森最擅长的就是用吸吮SO（-10）

         

虽然，还好在SO，Jason是最坏的情况做坏事（+10）

  

不容易的，是吧？ ：）

我不希望有人来解释这个算法给我，但我相信已经有对这样的事情在学术界的地方很多工作。如果你能指出我的一些文章或研究，我会喜欢它。

感谢。

解决方案

有自然语言处理的子场称为情感分析专门处理这个问题的领域。有商务工作在该地区做，因为消费类产品在网上用户论坛（UGC或用户生成内容），所以大量回顾了相当数量。还有的原型平台，文本分析所谓门从谢菲尔德大学，和一个名为的nltk 。二者都被视为柔性的，但不是非常高的性能。一方或另一方会是个不错的工作了自己的想法。

I need an algorithm to determine if a sentence, paragraph or article is negative or positive in tone... or better yet, how negative or positive.

For instance:

Jason is the worst SO user I have ever witnessed (-10)

Jason is an SO user (0)

Jason is the best SO user I have ever seen (+10)

Jason is the best at sucking with SO (-10)

While, okay at SO, Jason is the worst at doing bad (+10)

Not easy, huh? :)

I don't expect somebody to explain this algorithm to me, but I assume there is already much work on something like this in academia somewhere. If you can point me to some articles or research, I would love it.

Thanks.

解决方案

There is a sub-field of natural language processing called sentiment analysis that deals specifically with this problem domain. There is a fair amount of commercial work done in the area because consumer products are so heavily reviewed in online user forums (ugc or user-generated-content). There is also a prototype platform for text analytics called GATE from the university of sheffield, and a python project called nltk. Both are considered flexible, but not very high performance. One or the other might be good for working out your own ideas.

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