分类标记文本的问题，错误的预测？

Question

我正在使用 scikit-learn 提供的不同分类器和矢量化器，所以假设我有以下内容：

training = [["this was a good movie, 'POS'"],
      ["this was a bad movie, 'NEG'"],
      ["i went to the movies, 'NEU'"], 
      ["this movie was very exiting it was great, 'POS'"], 
      ["this is a boring film, 'NEG'"]
        ,........................,
          [" N-sentence, 'LABEL'"]]

#Where each element of the list is another list that have documents, then.

splitted = [#remove the tags from training]

from sklearn.feature_extraction.text import HashingVectorizer
X = HashingVectorizer(
    tokenizer=lambda  doc: doc, lowercase=False).fit_transform(splitted)

print X.toarray()

然后我有这个向量表示：

[[ 0.  0.  0. ...,  0.  0.  0.]
 [ 0.  0.  0. ...,  0.  0.  0.]
 [ 0.  0.  0. ...,  0.  0.  0.]
 [ 0.  0.  0. ...,  0.  0.  0.]
 [ 0.  0.  0. ...,  0.  0.  0.]]

这个问题是我不知道我是否对语料库进行了正确的向量化，那么：

#This is the test corpus:
test = ["I don't like this movie it sucks it doesn't liked me"]

#I vectorize the corpus with hashing vectorizer
Y = HashingVectorizer(
    tokenizer=lambda  doc: doc, lowercase=False).fit_transform(test)

然后我打印Y:

[[ 0.  0.  0. ...,  0.  0.  0.]]

然后

y = [x[-1]for x in training]

#import SVM and classify
from sklearn.svm import SVC
svm = SVC()
svm.fit(X, y)
result = svm.predict(X)
print "\nThe opinion is:\n",result

这就是问题所在，我在 [NEG] 中插入了以下内容，这实际上是正确的预测：

["this was a good movie, 'POS'"]

我想我没有正确矢量化 training 或 y 目标是错误的，谁能帮助我了解正在发生的事情以及我应该如何矢量化 training 以获得正确的预测？

Answer 1

我会让你把训练数据变成预期的格式：

training = ["this was a good movie",
            "this was a bad movie",
            "i went to the movies",
            "this movie was very exiting it was great", 
            "this is a boring film"]

labels = ['POS', 'NEG', 'NEU', 'POS', 'NEG']

特征提取

>>> from sklearn.feature_extraction.text import HashingVectorizer
>>> vect = HashingVectorizer(n_features=5, stop_words='english', non_negative=True)
>>> X_train = vect.fit_transform(training)
>>> X_train.toarray()
[[ 0.          0.70710678  0.          0.          0.70710678]
 [ 0.70710678  0.70710678  0.          0.          0.        ]
 [ 0.          0.          0.          0.          0.        ]
 [ 0.          0.89442719  0.          0.4472136   0.        ]
 [ 1.          0.          0.          0.          0.        ]]

对于更大的语料库，您应该增加n_features 以避免冲突，我使用了 5 以便可以可视化生成的矩阵。另请注意，我使用了stop_words='english'，我认为在这么少的例子中去掉停用词很重要，否则你可能会混淆分类器。

模型训练

from sklearn.svm import SVC

model = SVC()
model.fit(X_train, labels)

预测

>>> test = ["I don't like this movie it sucks it doesn't liked me"]
>>> X_pred = vect.transform(test)
>>> model.predict(X_pred)
['NEG']

>>> test = ["I think it was a good movie"]
>>> X_pred = vect.transform(test)
>>> model.predict(X_pred)
['POS']

编辑：请注意，第一个测试示例的正确分类只是一个幸运的巧合，因为我没有看到任何可以从训练集中学到的单词是否定的。在第二个示例中，单词 good 可能触发了正分类。