Question

我正在尝试进行情绪分析。我已经设法通过nltk使用朴素贝叶斯来分类负面和正面推文的语料库。但是我不希望每次运行此程序时都要经历运行此分类器的过程，因此我尝试使用pickle进行保存，然后将分类器加载到不同的脚本中。但是，当我尝试运行脚本时，它返回错误NameError：未定义name分类器，尽管我认为它是通过def load_classifier（）定义的：

我的代码如下：

import nltk, pickle
from nltk.corpus import stopwords

customstopwords = ['']

p = open('xxx', 'r')
postxt = p.readlines()

n = open('xxx', 'r')
negtxt = n.readlines()

neglist = []
poslist = []

for i in range(0,len(negtxt)):
    neglist.append('negative')

for i in range(0,len(postxt)):
    poslist.append('positive')

postagged = zip(postxt, poslist)
negtagged = zip(negtxt, neglist)


taggedtweets = postagged + negtagged

tweets = []

for (word, sentiment) in taggedtweets:
    word_filter = [i.lower() for i in word.split()]
    tweets.append((word_filter, sentiment))

def getwords(tweets):
    allwords = []
    for (words, sentiment) in tweets:
            allwords.extend(words)
    return allwords

def getwordfeatures(listoftweets):
    wordfreq = nltk.FreqDist(listoftweets)
    words = wordfreq.keys()
    return words

wordlist = [i for i in getwordfeatures(getwords(tweets)) if not i in                  stopwords.words('english')]
wordlist = [i for i in getwordfeatures(getwords(tweets)) if not i in customstopwords]


def feature_extractor(doc):
    docwords = set(doc)
    features = {}
    for i in wordlist:
        features['contains(%s)' % i] = (i in docwords)
    return features


training_set = nltk.classify.apply_features(feature_extractor, tweets)

def load_classifier():
   f = open('my_classifier.pickle', 'rb')
   classifier = pickle.load(f)
   f.close
   return classifier

while True:
    input = raw_input('I hate this film')
    if input == 'exit':
        break
    elif input == 'informfeatures':
        print classifier.show_most_informative_features(n=30)
        continue
    else:
        input = input.lower()
        input = input.split()
        print '\nSentiment is ' + classifier.classify(feature_extractor(input)) + ' in that sentence.\n'

p.close()
n.close()

任何帮助都会很棒，脚本似乎会在返回错误之前将它打印到'\ nSentiment is'+ classifier.classify（feature_extractor（input））+'中。\ n'“

Answer 1

嗯，你已经声明并定义了 load_classifier()方法，但从未调用过它，使用它来分配变量。这意味着，到时候，执行到达print '\nSentiment is... '行，没有变量名classifier。当然，执行会引发异常。

在while循环之前添加行classifier = load_classifier()。（没有任何缩进）

使用Pickle加载分类器？

1 个答案: