1
나는 긍정적이고 부정적인 문장이있다. 다른 문장에 대한 감정을 조사하기 위해 NaiveBayesClassifier를 교육하기 위해 Python NLTK를 사용하는 것이 매우 간단합니다.Python NLTK 감정이 올바르지 않다.
이 코드를 사용하려고하지만 결과는 항상 긍정적입니다. http://www.sjwhitworth.com/sentiment-analysis-in-python-using-nltk/
저는 파이썬에서 매우 새로운 부분이므로, 복사 할 때 코드에 실수가 있습니다.
import nltk
import math
import re
import sys
import os
import codecs
reload(sys)
sys.setdefaultencoding('utf-8')
from nltk.corpus import stopwords
__location__ = os.path.realpath(
os.path.join(os.getcwd(), os.path.dirname(__file__)))
postweet = __location__ + "/postweet.txt"
negtweet = __location__ + "/negtweet.txt"
customstopwords = ['band', 'they', 'them']
#Load positive tweets into a list
p = open(postweet, 'r')
postxt = p.readlines()
#Load negative tweets into a list
n = open(negtweet, 'r')
negtxt = n.readlines()
neglist = []
poslist = []
#Create a list of 'negatives' with the exact length of our negative tweet list.
for i in range(0,len(negtxt)):
neglist.append('negative')
#Likewise for positive.
for i in range(0,len(postxt)):
poslist.append('positive')
#Creates a list of tuples, with sentiment tagged.
postagged = zip(postxt, poslist)
negtagged = zip(negtxt, neglist)
#Combines all of the tagged tweets to one large list.
taggedtweets = postagged + negtagged
tweets = []
#Create a list of words in the tweet, within a tuple.
for (word, sentiment) in taggedtweets:
word_filter = [i.lower() for i in word.split()]
tweets.append((word_filter, sentiment))
#Pull out all of the words in a list of tagged tweets, formatted in tuples.
def getwords(tweets):
allwords = []
for (words, sentiment) in tweets:
allwords.extend(words)
return allwords
#Order a list of tweets by their frequency.
def getwordfeatures(listoftweets):
#Print out wordfreq if you want to have a look at the individual counts of words.
wordfreq = nltk.FreqDist(listoftweets)
words = wordfreq.keys()
return words
#Calls above functions - gives us list of the words in the tweets, ordered by freq.
print getwordfeatures(getwords(tweets))
wordlist = []
wordlist = [i for i in wordlist if not i in stopwords.words('english')]
wordlist = [i for i in wordlist 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
#Creates a training set - classifier learns distribution of true/falses in the input.
training_set = nltk.classify.apply_features(feature_extractor, tweets)
classifier = nltk.NaiveBayesClassifier.train(training_set)
print classifier.show_most_informative_features(n=30)
while True:
input = raw_input('ads')
if input == 'exit':
break
elif input == 'informfeatures':
print classifier.show_most_informative_features(n=30)
continue
else:
input = input.lower()
input = input.split()
print '\nWe think that the sentiment was ' + classifier.classify(feature_extractor(input)) + ' in that sentence.\n'
p.close()
n.close()
이것은 단지 오류 코드입니까? 또는 무엇이 문제인가. 문제가 그것이 인쇄 classifier.show_most_informative_features(n=30)을 인쇄해야합니다 시작하지만 난이 얻는 결과는이 힌트를 줄 수없는 경우 대부분의 유익한 기능 없음
경우.
감사합니다.