Python文本处理:AttributeError:'list'对象没有属性'lower'

Question

我是Python和Stackoverflow的新手(请温柔),我正在努力学习如何进行情绪分析.我正在使用我在教程中找到的代码组合,这里:Python - AttributeError:'list'对象没有属性但是,我一直在

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'`

这是我的代码:

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)

任何帮助都将受到大力赞赏.

Answer 1

csv阅读器的结果是一个列表,lower仅适用于字符串.据推测它是一个字符串列表,所以有两个选项.您可以调用lower每个元素,或将列表转换为字符串然后调用lower它.

# the first approach
[item.lower() for item in tweet]

# the second approach
' '.join(tweet).lower()

但更合理(没有更多信息很难说)你实际上只想要一个项目列表.有点像:

for row in reader:
    processedTestTweet = processTweet(row[0]) # Again, can't know if this is actually correct without seeing the file

另外,猜测你没有像你认为的那样使用csv阅读器,因为现在你每次都在一个单独的例子上训练一个朴素的贝叶斯分类器,然后让它预测它被训练的一个例子.也许解释你想要做什么？