我以前在堆栈上看到过这个问题，但解决方案对我不起作用，请参阅Save and Load testing contrast Naive Bayes Classifier in NLTK in another method。我很困惑为什么当我加载腌制分类器而不是仅仅在同一个程序中进行训练和分类时，我的准确性有很大不同。第一个代码块调用腌制分类器，第二个代码块一起进行所有训练和分类。第二种方法的准确率为 99%，而第一种方法的准确率为 81%……
Academic_classifier= None
Academic_classifier=pickle.load(open('Academic_classifier.pickle','rb'))
tweets=[]
readdata=csv.reader(open('C:\Users\Troy\Documents\Data\Gold_test.csv','r'))
for row in readdata:
tweets.append(row)
Header = tweets[0]
tweets.pop(0)
Academic_test_tweets=tweets[:]
Tweets=[]
for (words, sentiment) in tweets:
bigram=[]
bigram_list=[]
words_filtered = [e.lower() for e in WordPunctTokenizer().tokenize(words) if len(e) >= 3]
words_filtered=[re.sub(r'(.)\1+', r'\1\1', e) for e in words_filtered if len(e)>=3]
bigram_words=bigrams(words_filtered)
for x in bigram_words:
bigram.append(x)
for (bi) in bigram:
bigram_word=bi[0]+bi[1]
bigram_list.append(bigram_word)
list_to_append=words_filtered+bigram_list
Tweets.append((list_to_append, sentiment))
Academic_test_tweets_words=Tweets[:]
word_features = get_word_features(get_words_in_tweets(Academic_test_tweets_words))
Academic_test_set = nltk.classify.apply_features(extract_features,Academic_test_tweets_words)
print(nltk.classify.accuracy(Academic_classifier, Academic_test_set), 'tweet corpus used in academic paper Sentiment Analysis on the Social Networks Using Stream Algorithms Authors: Nathan Aston, Timothy Munson, Jacob Liddle, Garrett Hartshaw, Dane Livingston, Wei Hu *compare to their accuracy of 87.5%')
与我训练和测试准确性的这段代码相反。我对所有内容都使用相同的定义，所以我知道问题不在于定义。唯一的区别是腌制分类器......发生了什么？
tweets=[]
readdata=csv.reader(open('C:\Users\Troy\Documents\Data\Gold_test.csv','r'))
for row in readdata:
tweets.append(row)
Header = tweets[0]
tweets.pop(0)
Academic_test_tweets=tweets[:]
Tweets=[]
for (words, sentiment) in tweets:
bigram=[]
bigram_list=[]
words_filtered = [e.lower() for e in WordPunctTokenizer().tokenize(words) if len(e) >= 3]
words_filtered=[re.sub(r'(.)\1+', r'\1\1', e) for e in words_filtered if len(e)>=3]
bigram_words=bigrams(words_filtered)
for x in bigram_words:
bigram.append(x)
for (bi) in bigram:
bigram_word=bi[0]+bi[1]
bigram_list.append(bigram_word)
list_to_append=words_filtered+bigram_list
Tweets.append((list_to_append, sentiment))
Academic_test_tweets_words=Tweets[:]
word_features = get_word_features(get_words_in_tweets(Academic_test_tweets_words))
Academic_test_set = nltk.classify.apply_features(extract_features,Academic_test_tweets_words)
tweets=[]
readdata=csv.reader(open('C:\Users\Troy\Documents\Data\Gold_train.csv','r'))
for row in readdata:
tweets.append(row)
Header = tweets[0]
tweets.pop(0)
AcademicTweets=tweets[:]
Tweets=[]
for (words, sentiment) in tweets:
bigram=[]
bigram_list=[]
words_filtered = [e.lower() for e in WordPunctTokenizer().tokenize(words) if len(e) >= 3]
words_filtered=[re.sub(r'(.)\1+', r'\1\1', e) for e in words_filtered if len(e)>=3]
bigram_words=bigrams(words_filtered)
for x in bigram_words:
bigram.append(x)
for (bi) in bigram:
bigram_word=bi[0]+bi[1]
bigram_list.append(bigram_word)
list_to_append=words_filtered+bigram_list
Tweets.append((list_to_append, sentiment))
AcademicWords=Tweets[:]
word_features = get_word_features(get_words_in_tweets(AcademicWords))
Academic_training_set = nltk.classify.apply_features(extract_features,AcademicWords)
Academic_classifier = nltk.NaiveBayesClassifier.train(Academic_training_set)
#Negative_classifier.show_most_informative_features(1)
print(nltk.classify.accuracy(Academic_classifier, Academic_test_set), 'tweet corpus used in academic paper Sentiment Analysis on the Social Networks Using Stream Algorithms Authors: Nathan Aston, Timothy Munson, Jacob Liddle, Garrett Hartshaw, Dane Livingston, Wei Hu *compare to their accuracy of 87.5%')
pickle.dump(Academic_classifier, open('Academic_classifier.pickle','wb'))