nltk NaiveBayesClassifier情绪分析培训
nlp
nltk
python
4
0

我正在使用句子训练Python中的NaiveBayesClassifier ,它给了我下面的错误。我不知道错误可能是什么,任何帮助都将是很好的。

我尝试了许多其他输入格式,但错误仍然存在。下面给出的代码:

from text.classifiers import NaiveBayesClassifier
from text.blob import TextBlob
train = [('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') ]

test = [('The beer was good.', 'pos'),
        ('I do not enjoy my job', 'neg'),
        ("I ain't feeling dandy today.", 'neg'),
        ("I feel amazing!", 'pos'),
        ('Gary is a friend of mine.', 'pos'),
        ("I can't believe I'm doing this.", 'neg') ]
classifier = nltk.NaiveBayesClassifier.train(train)

我包括下面的追溯。

Traceback (most recent call last):
  File "C:\Users\5460\Desktop\train01.py", line 15, in <module>
    all_words = set(word.lower() for passage in train for word in word_tokenize(passage[0]))
  File "C:\Users\5460\Desktop\train01.py", line 15, in <genexpr>
    all_words = set(word.lower() for passage in train for word in word_tokenize(passage[0]))
  File "C:\Python27\lib\site-packages\nltk\tokenize\__init__.py", line 87, in word_tokenize
    return _word_tokenize(text)
  File "C:\Python27\lib\site-packages\nltk\tokenize\treebank.py", line 67, in tokenize
    text = re.sub(r'^\"', r'``', text)
  File "C:\Python27\lib\re.py", line 151, in sub
    return _compile(pattern, flags).sub(repl, string, count)
TypeError: expected string or buffer
参考资料:
Stack Overflow
收藏
评论
共 3 个回答
高赞 时间 活跃

您需要更改数据结构。这是目前的train清单:

>>> train = [('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')]

问题是,每个元组的第一个元素应该是功能字典。因此,我将您的列表更改为分类器可以使用的数据结构:

>>> from nltk.tokenize import word_tokenize # or use some other tokenizer
>>> all_words = set(word.lower() for passage in train for word in word_tokenize(passage[0]))
>>> t = [({word: (word in word_tokenize(x[0])) for word in all_words}, x[1]) for x in train]

现在,您的数据的结构应如下所示:

>>> t
[({'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'), . . .]

注意,每个元组的第一个元素现在是字典。现在您的数据已经到位,每个元组的第一个元素是字典,您可以像这样训练分类器:

>>> import nltk
>>> classifier = nltk.NaiveBayesClassifier.train(t)
>>> classifier.show_most_informative_features()
Most Informative Features
                    this = True              neg : pos    =      2.3 : 1.0
                    this = False             pos : neg    =      1.8 : 1.0
                      an = False             neg : pos    =      1.6 : 1.0
                       . = True              pos : neg    =      1.4 : 1.0
                       . = False             neg : pos    =      1.4 : 1.0
                 awesome = False             neg : pos    =      1.2 : 1.0
                      of = False             pos : neg    =      1.2 : 1.0
                    feel = False             neg : pos    =      1.2 : 1.0
                   place = False             neg : pos    =      1.2 : 1.0
                horrible = False             pos : neg    =      1.2 : 1.0

如果要使用分类器,可以这样进行。首先,从一个测试句子开始:

>>> test_sentence = "This is the best band I've ever heard!"

然后,您标记该句子并找出该句子与all_words共享的单词。这些构成了句子的特征。

>>> test_sent_features = {word: (word in word_tokenize(test_sentence.lower())) for word in all_words}

现在,您的功能将如下所示:

>>> test_sent_features
{'love': False, 'deal': False, 'tired': False, 'feel': False, 'is': True, 'am': False, 'an': False, 'sandwich': False, 'ca': False, 'best': True, '!': True, 'what': False, 'i': True, '.': False, '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, 'this': True, 'of': False, 'work': False, "n't": False, 'these': False, 'stuff': False, 'place': False, 'my': False, 'view': False}

然后,您只需对这些功能进行分类:

>>> classifier.classify(test_sent_features)
'pos' # note 'best' == True in the sentence features above

这个测试句子似乎是肯定的。

收藏
评论

@ 275365有关NLTK贝叶斯分类器的数据结构的教程很棒。从更高的角度来看,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),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,这里的vocabulary与@ 275365的all_word相同

>>> 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),我们想说一个特征(即词汇中的单词)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}

但是我们还想告诉分类器,句子中不存在哪个单词,而是词汇中的单词,因此对于每个数据点,我们列出词汇中所有可能的单词,并说出一个单词是否存在:

>>> 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}

但是,由于这两次遍历词汇表,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}

因此,对于每个句子,我们想告诉每个句子的分类器哪个词存在,哪个词不存在,并为其赋予pos / neg标记。我们可以称它为feature_set ,它是一个由x (如上所示)及其标记组成的元组。

>>> 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中的这些功能和标签提供给分类器以对其进行训练:

from nltk import NaiveBayesClassifier as nbc
classifier = nbc.train(feature_set)

现在,您已经拥有训练有素的分类器,并且想要标记新句子时,您必须“特征化”新句子,以查看新句子中哪个单词属于分类器训练的词汇表:

>>> 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:从上面的步骤中可以看到,朴素的贝叶斯分类器无法处理词汇表中的单词,因为foobar标记在特性化后会消失。

然后,您将特征化的测试句子输入分类器,并要求其进行分类:

>>> classifier.classify(featurized_test_sentence)
'pos'

希望这可以更清晰地说明如何将数据输入到NLTK的朴素贝叶斯分类器中进行情感分析。这是完整的代码,没有注释和演练:

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)
收藏
评论

看来您正在尝试使用TextBlob,但正在训练NLTK NaiveBayesClassifier,如其他答案所述,必须将其传递给功能词典。

TextBlob具有默认的特征提取器,该特征提取器指示训练集中的哪些词包含在文档中(如其他答案所示)。因此,TextBlob允许您按原样传递数据。

from textblob.classifiers import NaiveBayesClassifier

train = [('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') ] 
test = [
        ('The beer was good.', 'pos'),
        ('I do not enjoy my job', 'neg'),
        ("I ain't feeling dandy today.", 'neg'),
        ("I feel amazing!", 'pos'),
        ('Gary is a friend of mine.', 'pos'),
        ("I can't believe I'm doing this.", 'neg') ] 


classifier = NaiveBayesClassifier(train)  # Pass in data as is
# When classifying text, features are extracted automatically
classifier.classify("This is an amazing library!")  # => 'pos'

当然,简单的默认提取器并不适合所有问题。如果要提取特征,只需编写一个函数,该函数将文本字符串作为输入,然后输出特征字典并将其传递给分类器。

classifier = NaiveBayesClassifier(train, feature_extractor=my_extractor_func)

我鼓励您在此处查看简短的TextBlob分类器教程: http ://textblob.readthedocs.org/en/latest/classifiers.html

收藏
评论
新手导航
  • 社区规范
  • 提出问题
  • 进行投票
  • 个人资料
  • 优化问题
  • 回答问题

关于我们

常见问题

内容许可

联系我们

@2020 AskGo
京ICP备20001863号