带朴素贝叶斯分类的n-grams

Question

我是python新手，需要帮助！ 我在练习python NLTK文本分类。 下面是我正在练习的代码示例 http://www.laurentluce.com/posts/twitter-sentiment-analysis-using-python-and-nltk/

我试过这个

from nltk import bigrams
from nltk.probability import ELEProbDist, FreqDist
from nltk import NaiveBayesClassifier
from collections import defaultdict

train_samples = {}

with file ('positive.txt', 'rt') as f:
   for line in f.readlines():
       train_samples[line]='pos'

with file ('negative.txt', 'rt') as d:
   for line in d.readlines():
       train_samples[line]='neg'

f=open("test.txt", "r")
test_samples=f.readlines()

def bigramReturner(text):
    tweetString = text.lower()
    bigramFeatureVector = {}
    for item in bigrams(tweetString.split()):
        bigramFeatureVector.append(' '.join(item))
    return bigramFeatureVector

def get_labeled_features(samples):
    word_freqs = {}
    for text, label in train_samples.items():
        tokens = text.split()
        for token in tokens:
            if token not in word_freqs:
                word_freqs[token] = {'pos': 0, 'neg': 0}
            word_freqs[token][label] += 1
    return word_freqs


def get_label_probdist(labeled_features):
    label_fd = FreqDist()
    for item,counts in labeled_features.items():
        for label in ['neg','pos']:
            if counts[label] > 0:
                label_fd.inc(label)
    label_probdist = ELEProbDist(label_fd)
    return label_probdist


def get_feature_probdist(labeled_features):
    feature_freqdist = defaultdict(FreqDist)
    feature_values = defaultdict(set)
    num_samples = len(train_samples) / 2
    for token, counts in labeled_features.items():
        for label in ['neg','pos']:
            feature_freqdist[label, token].inc(True, count=counts[label])
            feature_freqdist[label, token].inc(None, num_samples - counts[label])
            feature_values[token].add(None)
            feature_values[token].add(True)
    for item in feature_freqdist.items():
        print item[0],item[1]
    feature_probdist = {}
    for ((label, fname), freqdist) in feature_freqdist.items():
        probdist = ELEProbDist(freqdist, bins=len(feature_values[fname]))
        feature_probdist[label,fname] = probdist
    return feature_probdist



labeled_features = get_labeled_features(train_samples)

label_probdist = get_label_probdist(labeled_features)

feature_probdist = get_feature_probdist(labeled_features)

classifier = NaiveBayesClassifier(label_probdist, feature_probdist)

for sample in test_samples:
    print "%s | %s" % (sample, classifier.classify(bigramReturner(sample)))

但为什么会犯这个错误？

    Traceback (most recent call last):
  File "C:\python\naive_test.py", line 76, in <module>
    print "%s | %s" % (sample, classifier.classify(bigramReturner(sample)))
  File "C:\python\naive_test.py", line 23, in bigramReturner
    bigramFeatureVector.append(' '.join(item))
AttributeError: 'dict' object has no attribute 'append'

Answer 1

bigram特征向量遵循与unigram特征向量完全相同的原理。因此，就像您提到的教程一样，您必须检查您将使用的任何文档中是否存在bigram特性。

至于bigram特性以及如何提取它们，我已经为它编写了下面的代码。您可以简单地采用它们来更改教程中的变量“tweets”。

import nltk
text = "Hi, I want to get the bigram list of this string"
for item in nltk.bigrams (text.split()): print ' '.join(item)

你不必打印它们，只需把它们附加到“tweets”列表中就可以了！我希望这能有足够的帮助。否则，如果你还有问题请告诉我。

请注意，在情感分析等应用中，有些研究人员倾向于标记单词并删除标点符号，而有些则不这样做。根据经验，我知道如果不删除标点符号，Naive bayes的工作原理几乎相同，但是支持向量机的准确率会降低。你可能需要反复考虑这些东西，并决定哪些在你的数据集上更有效。

编辑1:

有一本书叫做“用Python进行自然语言处理”，我可以推荐给你。它包含大字图的例子以及一些练习。不过，我认为你甚至可以解决这个案子没有它。选择大词a特征的想法是，我们想知道单词a出现在我们的语料库中的可能性，然后是单词B

"I drive a truck"

单词unigram features是这4个单词中的每一个，而单词bigram features是：

["I drive", "drive a", "a truck"]

现在你想用这3个作为你的功能。所以下面的代码函数将字符串的所有bigram放入名为bigramFeatureVector的列表中。

def bigramReturner (tweetString):
  tweetString = tweetString.lower()
  tweetString = removePunctuation (tweetString)
  bigramFeatureVector = []
  for item in nltk.bigrams(tweetString.split()):
      bigramFeatureVector.append(' '.join(item))
  return bigramFeatureVector

注意，您必须编写自己的removePunctuation函数。上面函数的输出是bigram特征向量。您将像您在教程中提到的那样对待unigram特征向量。