确定陈述/文本的正面或负面程度的算法 [英] Algorithm to determine how positive or negative a statement/text is

问题描述

我需要一种算法来确定一个句子、段落或文章在语气上是消极还是积极......或者更好的是，如何消极或积极.

例如:

<块引用><块引用>

Jason 是我见过的最糟糕的 SO 用户 (-10)

Jason 是 SO 用户 (0)

Jason 是我见过的最好的 SO 用户 (+10)

Jason 最擅长使用 SO (-10)

虽然，在 SO 上还可以，但 Jason 在做坏事方面是最糟糕的 (+10)

不容易吧?:)

我不希望有人向我解释这个算法，但我认为在某个地方的学术界已经有很多类似的工作.如果你能指点我一些文章或研究，我会很高兴的.

谢谢.

解决方案

自然语言处理有一个子领域叫做 情感分析 专门处理这个问题域.由于在线用户论坛(ugc 或用户生成的内容)对消费品进行了大量审查，因此在该领域进行了大量的商业工作.还有一个来自谢菲尔德大学的名为GATE的文本分析原型平台，以及一个名为nltk.两者都被认为是灵活的，但不是非常高的性能.一种或另一种可能有助于实现您自己的想法.

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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