NLTK在python中文字所表达的情感预测

NLTK是python环境下NLP工具包，包含了丰富的文本处理和文本挖掘API。

自然语言处理是计算机科学领域与人工智能领域中的一个重要方向。自然语言工具箱（NLTK，Natural Language Toolkit）
是一个基于Python语言的类库，它也是当前最为流行的自然语言编程与开发工具。在进行自然语言处理研究和应用时，
恰当利用NLTK中提供的函数可以大幅度地提高效率。本文就将通过一些实例来向读者介绍NLTK的使用。

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from nltk.classify import NaiveBayesClassifier

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

# 数据准备
positive_vocab = ['awesome', 'outstanding', 'fantastic', 'terrific', 'good', 'nice', 'great', ':)', 'incredible', 'like']
negative_vocab = ['bad', 'terrible', 'useless', 'hate', ':(', 'motherfucker']
neutral_vocab = ['movie', 'the', 'sound', 'was', 'is', 'actors', 'did', 'know', 'words', 'not']

# 特征提取
positive_features = [(word_feats(pos), 'pos') for pos in positive_vocab]
negative_features = [(word_feats(neg), 'neg') for neg in negative_vocab]
neutral_features = [(word_feats(neu), 'neu') for neu in neutral_vocab]

train_set = negative_features + positive_features + neutral_features
# 训练
classifier = NaiveBayesClassifier.train(train_set)

# 测试
neg = 0
pos = 0
sentence = "I like the wonderful movie, it is awesome!!!"
sentence = sentence.lower()
words = sentence.split(' ')
for word in words:
        classResult = classifier.classify(word_feats(word))
        if classResult == 'neg':
            neg = neg + 1
        if classResult == 'pos':
            pos = pos + 1

print('积极: ' + str(float(pos) / len(words)))
print('消极: ' + str(float(neg) / len(words)))

/# positive:0.625
negative”0.375