使用NaiveBayesClassifier给出负面结果的感伤分析

Question

我试图从一个积极或消极的词中找到情绪。但不知何故，我的代码总是回归负面情绪。

from nltk.classify import NaiveBayesClassifier


def word_feats(words):
    return dict([(word, True) for word in words])


positive_vocab = ['awesome', 'outstanding', 'fantastic', 'terrific', 'good', 'nice', 'great', ':)', 'liked']
negative_vocab = ['bad', 'terrible', 'useless', 'hate', ':(']

positive_features = [(word_feats(pos), 'pos') for pos in positive_vocab]

negative_features = [(word_feats(neg), 'neg') for neg in negative_vocab]


train_set = positive_features + negative_features


classifier = NaiveBayesClassifier.train(train_set)

# Predict
neg = 0
pos = 0
sentence = "awesome"
sentence = sentence.lower()
words = sentence.split(' ')
for word in words:
    print(word)
    classResult = classifier.classify(word_feats(word))
    print(classResult)
    if classResult == 'neg':
        neg = neg + 1
    if classResult == 'pos':
        pos = pos + 1
        # if classResult == 'neu':
        #     neu = neu + 1

print('Positive: ' + str(float(pos) / len(words)))
print('Negative: ' + str(float(neg) / len(words)))

上面的代码返回结果：

正面：0.0

否定：1.0

有人可以帮帮我吗？而且，我没有得到内部朴素的贝叶斯训练如何训练。