@275365 关于 NLTK 贝叶斯分类器数据结构的教程很棒。从更高的层面，我们可以将其视为，
我们有带有情感标签的输入句子：
training_data = [('I love this sandwich.', 'pos'),
('This is an amazing place!', 'pos'),
('I feel very good about these beers.', 'pos'),
('This is my best work.', 'pos'),
("What an awesome view", 'pos'),
('I do not like this restaurant', 'neg'),
('I am tired of this stuff.', 'neg'),
("I can't deal with this", 'neg'),
('He is my sworn enemy!', 'neg'),
('My boss is horrible.', 'neg')]
让我们将特征集视为单个单词，因此我们从训练数据中提取所有可能单词的列表（我们称之为词汇表），如下所示：
from nltk.tokenize import word_tokenize
from itertools import chain
vocabulary = set(chain(*[word_tokenize(i[0].lower()) for i in training_data]))
本质上，vocabulary这里是相同的@275365all_word
>>> all_words = set(word.lower() for passage in training_data for word in word_tokenize(passage[0]))
>>> vocabulary = set(chain(*[word_tokenize(i[0].lower()) for i in training_data]))
>>> print vocabulary == all_words
True
从每个数据点（即每个句子和 pos/neg 标签），我们想说明一个特征（即词汇表中的一个词）是否存在。
>>> sentence = word_tokenize('I love this sandwich.'.lower())
>>> print {i:True for i in vocabulary if i in sentence}
{'this': True, 'i': True, 'sandwich': True, 'love': True, '.': True}
但是我们也想告诉分类器哪个词不存在于句子中，但存在于词汇表中，所以对于每个数据点，我们列出词汇表中所有可能的词，并说出一个词是否存在：
>>> sentence = word_tokenize('I love this sandwich.'.lower())
>>> x = {i:True for i in vocabulary if i in sentence}
>>> y = {i:False for i in vocabulary if i not in sentence}
>>> x.update(y)
>>> print x
{'love': True, 'deal': False, 'tired': False, 'feel': False, 'is': False, 'am': False, 'an': False, 'good': False, 'best': False, '!': False, 'these': False, 'what': False, '.': True, 'amazing': False, 'horrible': False, 'sworn': False, 'ca': False, 'do': False, 'sandwich': True, 'very': False, 'boss': False, 'beers': False, 'not': False, 'with': False, 'he': False, 'enemy': False, 'about': False, 'like': False, 'restaurant': False, 'this': True, 'of': False, 'work': False, "n't": False, 'i': True, 'stuff': False, 'place': False, 'my': False, 'awesome': False, 'view': False}
但由于这会在词汇表中循环两次，因此这样做更有效：
>>> sentence = word_tokenize('I love this sandwich.'.lower())
>>> x = {i:(i in sentence) for i in vocabulary}
{'love': True, 'deal': False, 'tired': False, 'feel': False, 'is': False, 'am': False, 'an': False, 'good': False, 'best': False, '!': False, 'these': False, 'what': False, '.': True, 'amazing': False, 'horrible': False, 'sworn': False, 'ca': False, 'do': False, 'sandwich': True, 'very': False, 'boss': False, 'beers': False, 'not': False, 'with': False, 'he': False, 'enemy': False, 'about': False, 'like': False, 'restaurant': False, 'this': True, 'of': False, 'work': False, "n't": False, 'i': True, 'stuff': False, 'place': False, 'my': False, 'awesome': False, 'view': False}
所以对于每个句子，我们想告诉每个句子的分类器哪个单词存在，哪个单词不存在，并给它一个 pos/neg 标签。我们可以称之为 a feature_set，它是一个由 a x（如上所示）及其标签组成的元组。
>>> feature_set = [({i:(i in word_tokenize(sentence.lower())) for i in vocabulary},tag) for sentence, tag in training_data]
[({'this': True, 'love': True, 'deal': False, 'tired': False, 'feel': False, 'is': False, 'am': False, 'an': False, 'sandwich': True, 'ca': False, 'best': False, '!': False, 'what': False, '.': True, 'amazing': False, 'horrible': False, 'sworn': False, 'awesome': False, 'do': False, 'good': False, 'very': False, 'boss': False, 'beers': False, 'not': False, 'with': False, 'he': False, 'enemy': False, 'about': False, 'like': False, 'restaurant': False, 'these': False, 'of': False, 'work': False, "n't": False, 'i': False, 'stuff': False, 'place': False, 'my': False, 'view': False}, 'pos'), ...]
然后我们将 feature_set 中的这些特征和标签输入分类器来训练它：
from nltk import NaiveBayesClassifier as nbc
classifier = nbc.train(feature_set)
现在你有一个训练有素的分类器，当你想标记一个新句子时，你必须对新句子进行“特征化”，以查看新句子中的哪个单词在分类器训练的词汇表中：
>>> test_sentence = "This is the best band I've ever heard! foobar"
>>> featurized_test_sentence = {i:(i in word_tokenize(test_sentence.lower())) for i in vocabulary}
注意：正如您从上面的步骤中看到的，朴素贝叶斯分类器无法处理词汇表之外的单词，因为foobar标记在您对其进行特征化后消失了。
然后你将特征化的测试句子输入分类器并要求它进行分类：
>>> classifier.classify(featurized_test_sentence)
'pos'
希望这可以更清楚地说明如何将数据输入 NLTK 的朴素贝叶斯分类器以进行情感分析。这是没有注释和演练的完整代码：
from nltk import NaiveBayesClassifier as nbc
from nltk.tokenize import word_tokenize
from itertools import chain
training_data = [('I love this sandwich.', 'pos'),
('This is an amazing place!', 'pos'),
('I feel very good about these beers.', 'pos'),
('This is my best work.', 'pos'),
("What an awesome view", 'pos'),
('I do not like this restaurant', 'neg'),
('I am tired of this stuff.', 'neg'),
("I can't deal with this", 'neg'),
('He is my sworn enemy!', 'neg'),
('My boss is horrible.', 'neg')]
vocabulary = set(chain(*[word_tokenize(i[0].lower()) for i in training_data]))
feature_set = [({i:(i in word_tokenize(sentence.lower())) for i in vocabulary},tag) for sentence, tag in training_data]
classifier = nbc.train(feature_set)
test_sentence = "This is the best band I've ever heard!"
featurized_test_sentence = {i:(i in word_tokenize(test_sentence.lower())) for i in vocabulary}
print "test_sent:",test_sentence
print "tag:",classifier.classify(featurized_test_sentence)