如何训练斯坦福 NLP 情绪分析工具 [英] How to train the Stanford NLP Sentiment Analysis tool

问题描述

见鬼去吧！我正在使用斯坦福核心 NLP 包，我的目标是对推文直播进行情感分析.

按原样使用情绪分析工具会返回对文本态度"的非常糟糕的分析......许多正面被标记为中性，许多负面被评为正面.我已经在一个文本文件中获得了超过一百万条推文，但我不知道如何实际训练该工具并创建我自己的模型.

链接到斯坦福情绪分析页面

可以使用以下命令使用 PTB 格式数据集重新训练模型:"

java -mx8g edu.stanford.nlp.sentiment.SentimentTraining -numHid 25 -trainPath train.txt -devPath dev.txt -train -model model.ser.gz

来自 dev.txt 的示例(前导 4 代表 5 ... 4/5 正极中的极性)

(4(4(2A)(4(3(3)暖)(2,))(3搞笑)))(3(2,)(3(4(4搞))(2片)) (2 .))))

来自 test.txt 的示例

(3 (3 (2 If) (3 (2 you)) (3 (2有时) (2 (2 like) (3 (2 to) (3 (3 (2 go)) (2 (2)to) (2 (2 the) (2 电影)))) (3 (2 to) (3 (2 have) (4 fun))))))))) (2 (2 ,) (2 (2 Wasabi)) (3 (3 (2 is) (2 (2 a) (2 (3 good) (2 (2 place) (2 (2 to) (2 start)))))) (2 .)))))

来自 train.txt 的示例

(3 (2 (2) (2 Rock)) (4 (3 (2 is)) (4 (2 注定) (2 (2 (2 (2 (2) to) (2 (2 to))) (2 (2) (2 (2 21st) (2 (2 (2 Century) (2's)) (2 (3 new) (2 (2``) (2 Conan)))))))) (2 '')) (2 and)) (3 (2 that) (3 (2 he) (3 (2's) (3 (2 going) (3 (2 to)) (4 (3 (2 make)) (3 (3 (2 a) (3 splash)) (2 (2 even) (3更大)))) (2 (2 than) (2 (2 (2 (2 (1 (2) Arnold) (2 Schwarzenegger))) (2 ,)) (2 (2 Jean-Claud) (2 (2 Van) (2 Damme)))) (2 or)) (2 (2 Steven) (2 Segal))))))))))))) (2 .)))

我有两个问题要问.

每个文件的意义和区别是什么?Train.txt/Dev.txt/Test.txt ?

我将如何使用一个充满推文的原始、未解析的文本文件来训练我自己的模型?

我对 NLP 很陌生，所以如果我遗漏了任何必需的信息或任何东西，请批评！谢谢！

解决方案

每个文件的意义和区别是什么?Train.txt/Dev.txt/Test.txt ?

这是标准的机器学习术语.训练集用于(惊喜)训练模型.开发集用于调整模型可能具有的任何参数.您通常要做的是选择一个参数值，在训练集上训练模型，然后检查训练后的模型在开发集上的表现.然后选择另一个参数值并重复.此过程可帮助您为模型找到合理的参数值.

完成此操作后，您将继续测试模型在测试集上的表现.这是看不见的 - 您的模型以前从未遇到过任何此类数据.将测试集与训练和开发集分开是很重要的，否则您将有效地评估模型之前看到的数据.这是错误的，因为它不会让您了解模型的实际效果.

<块引用>

我将如何使用原始的、未解析的文本文件来训练我自己的模型推文?

您不能也不应该使用未解析的文档集进行训练.递归深度模型的全部意义(以及它表现如此出色的原因)在于它可以从解析树每个级别的情感注释中学习.你上面给出的句子可以这样格式化:

<前>(4(4(2A)(4(3(3暖)(2,))(3搞笑)))(3(2,)(3(4 (4 参与) (2 电影)) (2 .))))

通常，情感分析器使用文档级注释进行训练.你只有一个分数，这个分数适用于整个文档，忽略了文档中的短语可能表达不同情绪的事实.斯坦福团队付出了很多努力来注释文档中的每个短语以表达情感.例如，film 这个词本身在情感上是中性的:(2 film).然而，短语engaging film 是非常正面的:(4 (4Engaging) (2 film)) (2 .)

如果您已标记推文，则可以使用任何其他文档级情感分类器.stackoverflow 上的 sentiment-analysis 标签已经有一些很好的答案了，我就不在这里重复了.

PS 你给你的推文贴上了标签吗?一百万?如果你这样做了，我愿意为那个文件付给你很多钱:)

Hell everyone! I'm using the Stanford Core NLP package and my goal is to perform sentiment analysis on a live-stream of tweets.

Using the sentiment analysis tool as is returns a very poor analysis of text's 'attitude' .. many positives are labeled neutral, many negatives rated positive. I've gone ahead an acquired well over a million tweets in a text file, but I haven't a clue how to actually train the tool and create my own model.

Link to Stanford Sentiment Analysis page

"Models can be retrained using the following command using the PTB format dataset:"

java -mx8g edu.stanford.nlp.sentiment.SentimentTraining -numHid 25 -trainPath train.txt -devPath     dev.txt -train -model model.ser.gz

Sample from dev.txt (The leading 4 represents polarity out of 5 ... 4/5 positive)

(4 (4 (2 A) (4 (3 (3 warm) (2 ,)) (3 funny))) (3 (2 ,) (3 (4 (4 engaging) (2 film)) (2 .))))

Sample from test.txt

(3 (3 (2 If) (3 (2 you) (3 (2 sometimes) (2 (2 like) (3 (2 to) (3 (3 (2 go) (2 (2 to) (2 (2 the) (2 movies)))) (3 (2 to) (3 (2 have) (4 fun))))))))) (2 (2 ,) (2 (2 Wasabi) (3 (3 (2 is) (2 (2 a) (2 (3 good) (2 (2 place) (2 (2 to) (2 start)))))) (2 .)))))

Sample from train.txt

(3 (2 (2 The) (2 Rock)) (4 (3 (2 is) (4 (2 destined) (2 (2 (2 (2 (2 to) (2 (2 be) (2 (2 the) (2 (2 21st) (2 (2 (2 Century) (2 's)) (2 (3 new) (2 (2 ``) (2 Conan)))))))) (2 '')) (2 and)) (3 (2 that) (3 (2 he) (3 (2 's) (3 (2 going) (3 (2 to) (4 (3 (2 make) (3 (3 (2 a) (3 splash)) (2 (2 even) (3 greater)))) (2 (2 than) (2 (2 (2 (2 (1 (2 Arnold) (2 Schwarzenegger)) (2 ,)) (2 (2 Jean-Claud) (2 (2 Van) (2 Damme)))) (2 or)) (2 (2 Steven) (2 Segal))))))))))))) (2 .)))

I have two questions going forward.

What is the significance and difference between each file? Train.txt/Dev.txt/Test.txt ?

How would I train my own model with a raw, unparsed text file full of tweets?

I'm very new to NLP so if I am missing any required information or anything at all please critique! Thank you!

解决方案

What is the significance and difference between each file? Train.txt/Dev.txt/Test.txt ?

This is standard machine learning terminology. The train set is used to (surprise surprise) train a model. The development set is used to tune any parameters the model might have. What you would normally do is pick a parameter value, train a model on the training set, and then check how well the trained model does on the development set. You then pick another parameter value and repeat. This procedure helps you find reasonable parameter values for your model.

Once this is done, you proceed to test how well the model does on the test set. This is unseen- your model has never encountered any of that data before. It is important that the test set is separate from the training and development set, otherwise you are effectively evaluating a model on data it has seen before. This would be wrong as it will not give you an idea of how well the model really does.

How would I train my own model with a raw, unparsed text file full of tweets?

You can't and you shouldn't train using an unparsed set of documents. The entire point of the recursive deep model (and the reason it performs so well) is that it can learn from the sentiment annotations at every level of the parse tree. The sentence you have given above can be formatted like this:

(4 
    (4 
        (2 A) 
        (4 
            (3 (3 warm) (2 ,)) (3 funny)
        )
    ) 
    (3 
        (2 ,) 
        (3 
            (4 (4 engaging) (2 film)) (2 .)
        )
    )
)

Usually, a sentiment analyser is trained with document-level annotations. You only have one score, and this score applies to the document as a whole, ignoring the fact that the phrases in the document may express different sentiment. The Stanford team put a lot of effort into annotating every phrase in the document for sentiment. For example, the word film on its own is neutral in sentiment: (2 film). However, the phrase engaging film is very positive: (4 (4 engaging) (2 film)) (2 .)

If you have labelled tweets, you can use any other document-level sentiment classifier. The sentiment-analysis tag on stackoverflow already has some very good answers, I'm not going to repeat them here.

PS Did you label the tweets you have? All 1 million of them? If you did, I'd like to pay you a lot of money for that file :)

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