给定文本的情感分析 [英] Sentiment Analysis of given text

问题描述

该主题有许多线程。而且我张贴另一个。所有职位可能是一个方式做一个情感分析，但是我发现没有办法。

This topic has many thread. But also I am posting another one. All the post may be a way to do a sentiment analysis, but I found no way.

我要实现情感分析的方法做。因此，我要求给我一个方法。在我的研究，我发现，这个是无论如何使用。我估计贝叶斯算法是用来计算正词和否定词和计算句子是用袋词语的正或负的概率。

I want to implement the doing ways of sentiment analysis. So I would request to show me a way. During my research, I found that this is used anyway. I guess Bayesian algorithm is used to calculate positive words and negative words and calculate the probability of the sentence being positive or negative using bag of words.

这是仅适用于的话，我想我们要做的语言处理过。那么，有没有任何人谁拥有更多的知识？如果是的话，你能指导我一些算法，他们的链接，以供参考，让我可以实现。什么特别，可以帮助我在我的分析。

This is only for the words, I guess we have to do language processing too. So is there anyone who has more knowledge? If yes, can you guide me with some algorithms with their links for reference so that I can implement. Anything in particular that may help me in my analysis.

也可以你preFER我，我可以工作语言？有人说Java是同等耗费时间，使他们不推荐的Java一起工作。

Also can you prefer me language that I can work with? Some says Java is comparably time consuming so they don't recommend Java to work with.

任何形式的帮助是非常AP preciated。

Any type of help is much appreciated.

推荐答案

首先，情感分析是对不同层次，如文件，句子，短语，和要素水平上进行。哪一个是你的工作吗？有许多不同的方法给每个人。你可以找到一个很好的介绍到这个话题这里 。对于机器学习的方法，最重要的因素是功能的工程，它不是局限于袋的话。您可以在本教程我联系不同的应用程序等多种实用功能。你需要做的是什么语言处理取决于你想要使用的功能。您可能需要POS标记是否需要为你的功能，例如POS信息。

First of all, sentiment analysis is done on various levels, such as document, sentence, phrase, and feature level. Which one are you working on? There are many different approaches to each of them. You can find a very good intro to this topic here. For machine-learning approaches, the most important element is feature engineering and it's not limited to bag of words. You can find many other useful features in different applications from the tutorial I linked. What language processing you need to do depends on what features you want to use. You may need POS-tagging if POS information is needed for your features for example.

有关分类，你可以尝试支持向量机，最大熵，和朴素贝叶斯（可能为基准），这些都是经常使用的文献，对此您也可以找到一个pretty的COM prehensive列表中的链接。槌工具包包含ME和NB，如果你使用SVMlight，您可以轻松地特征的格式转换为马利特格式的功能。当然也有这些分类很多其他的实现。

For classifiers, you can try Support Vector Machines, Maximum Entropy, and Naive Bayes (probably as a baseline) and these are frequently used in the literature, about which you can also find a pretty comprehensive list in the link. The Mallet toolkit contains ME and NB, and if you use SVMlight, you can easily convert the feature formats to the Mallet format with a function. Of course there are many other implementations of these classifiers.

有关规则为基础的方法，逐点互信息被频繁使用，以及某些类型的打分为基础的方法等。

For rule-based methods, Pointwise Mutual Information is frequently used, and some kinds of scoring-based methods, etc.

希望这有助于。

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