Python traitement de texte: AttributeError: 'liste' de l'objet n'a pas d'attribut "inférieure"

Je suis nouveau à Python et à Stackoverflow(soyez doux) et suis en train d'apprendre comment faire une analyse de sentiment. Je suis en utilisant une combinaison de code que j'ai trouvé dans un tuto et ici: Python - AttributeError: 'liste' de l'objet n'a pas d'attribut Cependant, je reçois

Traceback (most recent call last):
    File "C:/Python27/training", line 111, in <module>
    processedTestTweet = processTweet(row)
  File "C:/Python27/training", line 19, in processTweet
    tweet = tweet.lower()
AttributeError: 'list' object has no attribute 'lower'`

C'est mon code:

import csv
#import regex
import re
import pprint
import nltk.classify
#start replaceTwoOrMore
def replaceTwoOrMore(s):
#look for 2 or more repetitions of character
pattern = re.compile(r"(.)\1{1,}", re.DOTALL)
return pattern.sub(r"\1\1", s)
# process the tweets
def processTweet(tweet):
#Convert to lower case
tweet = tweet.lower()
#Convert www.* or https?://* to URL
tweet = re.sub('((www\.[\s]+)|(https?://[^\s]+))','URL',tweet)
#Convert @username to AT_USER
tweet = re.sub('@[^\s]+','AT_USER',tweet)
#Remove additional white spaces
tweet = re.sub('[\s]+', ' ', tweet)
#Replace #word with word
tweet = re.sub(r'#([^\s]+)', r'\1', tweet)
#trim
tweet = tweet.strip('\'"')
return tweet
#start getStopWordList
def getStopWordList(stopWordListFileName):
#read the stopwords file and build a list
stopWords = []
stopWords.append('AT_USER')
stopWords.append('URL')
fp = open(stopWordListFileName, 'r')
line = fp.readline()
while line:
word = line.strip()
stopWords.append(word)
line = fp.readline()
fp.close()
return stopWords
def getFeatureVector(tweet, stopWords):
featureVector = []
words = tweet.split()
for w in words:
#replace two or more with two occurrences
w = replaceTwoOrMore(w)
#strip punctuation
w = w.strip('\'"?,.')
#check if it consists of only words
val = re.search(r"^[a-zA-Z][a-zA-Z0-9]*[a-zA-Z]+[a-zA-Z0-9]*$", w)
#ignore if it is a stopWord
if(w in stopWords or val is None):
continue
else:
featureVector.append(w.lower())
return featureVector
def extract_features(tweet):
tweet_words = set(tweet)
features = {}
for word in featureList:
features['contains(%s)' % word] = (word in tweet_words)
return features
#Read the tweets one by one and process it
inpTweets = csv.reader(open('C:/GsTraining.csv', 'rb'),
delimiter=',',
quotechar='|')
stopWords = getStopWordList('C:/stop.txt')
count = 0;
featureList = []
tweets = []
for row in inpTweets:
sentiment = row[0]
tweet = row[1]
processedTweet = processTweet(tweet)
featureVector = getFeatureVector(processedTweet, stopWords)
featureList.extend(featureVector)
tweets.append((featureVector, sentiment))
# Remove featureList duplicates
featureList = list(set(featureList))
# Generate the training set
training_set = nltk.classify.util.apply_features(extract_features, tweets)
# Train the Naive Bayes classifier
NBClassifier = nltk.NaiveBayesClassifier.train(training_set)
# Test the classifier
with open('C:/CleanedNewGSMain.txt', 'r') as csvinput:
with open('GSnewmain.csv', 'w') as csvoutput:
writer = csv.writer(csvoutput, lineterminator='\n')
reader = csv.reader(csvinput)
all=[]
row = next(reader)
for row in reader:
processedTestTweet = processTweet(row)
sentiment = NBClassifier.classify(
extract_features(getFeatureVector(processedTestTweet, stopWords)))
row.append(sentiment)
processTweet(row[1])
writer.writerows(all)

Toute aide serait très appréciée.

OriginalL'auteur user3670554 | 2014-05-23