LinearSVC没有正确分类情绪

Question

我正在尝试使用scikit-learn LinearSVC分类器制作情绪分析器。问题是分类器将每个句子分类为正面。另一个问题是 - 为什么函数predict（）会返回每个文本的分类标签列表？我认为它应该只返回一个文本/数字，这是分类标签。以下是代码中的示例。

vectorizer = TfidfVectorizer(input='content', decode_error='ignore')
vect_train_x = vectorizer.fit_transform(training_data) # this is actually a list of sentences

scaler = StandardScaler(with_mean=False) # I don't know why it should be False
X_train = scaler.fit_transform(vect_train_x)  # compute mean, std and transform training data as well

vect_test_x = vectorizer.transform(test) # the sentence that needs to be classified

X_test = scaler.transform(vect_test_x) 

clf = LinearSVC()
clf.fit(X_train, labels)
print vect_test_x
print clf.predict(X_test) # returning me a list of Positive => ['Positive' 'Positive' 'Positive' 'Positive' 'Positive' 'Positive']

如果你解释一下我究竟是什么不理解，我将非常感激。我试着阅读文档但没有任何例子我无法理解。我的训练数据包括10万个正面和10万个否定句子。

Answer 1

遇到这个问题，我遇到了同样的问题，解决了我的问题是先将X_test转换为列表，然后转换为np.array然后需要传递给'predict'函数

new_array = []
new_array.append(Input) #Input is string if reading from a file or from input()
X_test = np.array(new_array)
print clf.predict(X_test)