NLP:定性为“阳性"；与“负"句子 [英] NLP: Qualitatively "positive" vs "negative" sentence

问题描述

我需要您的帮助，以确定用于分析针对正面"与负面"的行业特定句子(即电影评论)的最佳方法.我以前见过像OpenNLP这样的库，但是它太底层了-它只是给我提供了基本的句子组成；我需要的是更高层次的结构: -希望有词表 -希望可以对我的数据进行训练

I need your help in determining the best approach for analyzing industry-specific sentences (i.e. movie reviews) for "positive" vs "negative". I've seen libraries such as OpenNLP before, but it's too low-level - it just gives me the basic sentence composition; what I need is a higher-level structure: - hopefully with wordlists - hopefully trainable on my set of data

谢谢！

推荐答案

您正在寻找的东西通常被冠以情感分析.通常，情绪分析无法处理讽刺或讽刺等微妙的微妙之处，但是如果您向其投入大量数据，它的效果会很好.

What you are looking for is commonly dubbed Sentiment Analysis. Typically, sentiment analysis is not able to handle delicate subtleties, like sarcasm or irony, but it fares pretty well if you throw a large set of data at it.

情感分析通常需要大量的预处理.至少是标记化，句子边界检测和词性标记.有时，语法分析可能很重要.正确地做到这一点是计算语言学研究的整个分支，除非您先花时间研究该领域，否则我不建议您提出自己的解决方案.

Sentiment analysis usually needs quite a bit of pre-processing. At least tokenization, sentence boundary detection and part-of-speech tagging. Sometimes, syntactic parsing can be important. Doing it properly is an entire branch of research in computational linguistics, and I wouldn't advise you with coming up with your own solution unless you take your time to study the field first.

OpenNLP有一些工具可以帮助情绪分析，但是如果您想要更严肃的事情，则应该研究 LingPipe 工具箱.它具有一些内置的SA功能，并且很好的教程.而且，您可以根据自己的数据训练它，但不要认为它是微不足道的:-).

OpenNLP has some tools to aid sentiment analysis, but if you want something more serious, you should look into the LingPipe toolkit. It has some built-in SA-functionality and a nice tutorial. And you can train it on your own set of data, but don't think that it is entirely trivial :-).

搜索该词可能还会为您提供一些可使用的资源.如果您有其他更具体的问题，请问，我正在密切关注nlp标签；-)

Googling for the term will probably also give you some resources to work with. If you have any more specific question, just ask, I'm watching the nlp-tag closely ;-)

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