Python 使用 Scikit Learn SVM 为文本分类准备数据
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Prepare data for text classification using Scikit Learn SVM
提问by user1906856
I'm trying to apply SVM from Scikit learn to classify the tweets I collected. So, there will be two categories, name them A and B. For now, I have all the tweets categorized in two text file, 'A.txt' and 'B.txt'. However, I'm not sure what type of data inputs the Scikit Learn SVM is asking for. I have a dictionary with labels (A and B) as its keys and a dictionary of features (unigrams) and their frequencies as values. Sorry, I'm really new to machine learning and not sure what I should do to get the SVM work. And I found that SVM is using numpy.ndarray as the type of its data input. Do I need to create one based on my own data? Should it be something like this?
我正在尝试应用 Scikit 中的 SVM 学习对我收集的推文进行分类。因此，将有两个类别，将它们命名为 A 和 B。现在，我将所有推文分类在两个文本文件“A.txt”和“B.txt”中。但是，我不确定 Scikit Learn SVM 要求什么类型的数据输入。我有一个以标签（A 和 B）作为键的字典和一个特征字典（一元组）及其频率作为值。抱歉，我对机器学习真的很陌生，不确定我应该怎么做才能让 SVM 工作。我发现 SVM 使用 numpy.ndarray 作为其数据输入的类型。我需要根据我自己的数据创建一个吗？应该是这样的吗？
Labels features frequency
A 'book' 54
B 'movies' 32
Any help is appreciated.
任何帮助表示赞赏。
回答by ogrisel
Have a look at the documentation on text feature extraction.
查看有关文本特征提取的文档。
Also have a look at the text classification example.
另请查看文本分类示例。
There is also a tutorial here:
这里还有一个教程：
http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html
http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html
In particular don't focus too much on SVM models (in particular not sklearn.svm.SVCthat is more interesting for kernel models hence not text classification): a simple Perceptron, LogisticRegression or Bernoulli naive Bayes models might work as good while being much faster to train.
特别是不要过分关注 SVM 模型（特别是sklearn.svm.SVC这对于内核模型来说不是更有趣，因此不是文本分类）：一个简单的感知器、LogisticRegression 或 Bernoulli 朴素贝叶斯模型可能同样有效，同时训练速度要快得多。
