TypeError:字符串索引必须是整数(用于情感分析的CSV文件中的文本数据预处理)

Question

我在这个网站上找到了一些代码:(https://towardsdatascience.com/creating-the-twitter-sentiment-analysis-program-in-python-with-naive-bayes-classification-672e5589a7ed)，用来在推特上进行情绪分析。我有我需要的csv文件，所以我没有构建它们，而是通过文件定义了变量。

preprocessedTrainingSet = tweetProcessor.processTweets(trainingData)

并追溯到这一行：

processedTweets.append((self._processTweet(tweet"text")，推特“label”))。

我不知道如何绕过这个问题，同时仍然保持代码的核心功能不变。

import pandas as pd
import re
from nltk.tokenize import word_tokenize
from string import punctuation 
from nltk.corpus import stopwords 
import twitter
import csv
import time
import nltk
nltk.download('stopwords')

testDataSet = pd.read_csv("Twitter data.csv")
print(testDataSet[0:4])
trainingData = pd.read_csv("full-corpus.csv")
print(trainingData[0:4])


class PreProcessTweets:
    def __init__(self):
        self._stopwords = set(stopwords.words('english') + list(punctuation) + ['AT_USER','URL'])

    def processTweets(self, list_of_tweets):
        processedTweets=[]
        for tweet in list_of_tweets:
            processedTweets.append((self._processTweet(tweet["text"]),tweet["label"]))
        return processedTweets

    def _processTweet(self, tweet):
        tweet = tweet.lower() # convert text to lower-case
        tweet = re.sub('((www\.[^\s]+)|(https?://[^\s]+))', 'URL', tweet) # remove URLs
        tweet = re.sub('@[^\s]+', 'AT_USER', tweet) # remove usernames
        tweet = re.sub(r'#([^\s]+)', r'\1', tweet) # remove the # in #hashtag
        tweet = word_tokenize(tweet) # remove repeated characters (helloooooooo into hello)
        return [word for word in tweet if word not in self._stopwords]

tweetProcessor = PreProcessTweets()
preprocessedTrainingSet = tweetProcessor.processTweets(trainingData)
preprocessedTestSet = tweetProcessor.processTweets(testDataSet)

Answer 1

没有你的实际数据很难判断，但我认为你通过彼此混淆了多种类型。

1. 当你加载csv-data时，你正在制作一个pandas数据帧。
2. 然后在processTweets方法中，您试图像列表一样遍历此数据帧。最后，在processTweets的for循环中，您访问列表的值，您将其称为'tweet'，您试图使用键‘

’和'label‘来访问'tweet’的值。不过，我怀疑你有没有字典。

我从这个site下载了一些推文。使用这些数据，我测试了您的代码，并进行了以下调整。

import pandas as pd
import re
from nltk.tokenize import word_tokenize
from string import punctuation
from nltk.corpus import stopwords
import nltk

#had to install 'punkt'
nltk.download('punkt')
nltk.download('stopwords')
testDataSet = pd.read_csv("data.csv")

# For testing if the code works I only used a TestDatasSet, and no trainingData.


class PreProcessTweets:
    def __init__(self):
        self._stopwords = set(stopwords.words('english') + list(punctuation) + ['AT_USER','URL'])

    # To make it clear I changed the parameter to df_of_tweets (df = dataframe)
    def processTweets(self, df_of_tweets):

        processedTweets=[]

        #turning the dataframe into lists
        # in my data I did not have a label, so I used sentiment instead.
        list_of_tweets = df_of_tweets.text.tolist()
        list_of_sentiment = df_of_tweets.sentiment.tolist()

        # using enumerate to keep track of the index of the tweets so I can use it to index the list of sentiment
        for index, tweet in enumerate(list_of_tweets):
            # adjusted the code here so that it takes values of the lists straight away.
            processedTweets.append((self._processTweet(tweet), list_of_sentiment[index]))
        return processedTweets

    def _processTweet(self, tweet):
        tweet = tweet.lower() # convert text to lower-case
        tweet = re.sub('((www\.[^\s]+)|(https?://[^\s]+))', 'URL', tweet) # remove URLs
        tweet = re.sub('@[^\s]+', 'AT_USER', tweet) # remove usernames
        tweet = re.sub(r'#([^\s]+)', r'\1', tweet) # remove the # in #hashtag
        tweet = word_tokenize(tweet) # remove repeated characters (helloooooooo into hello)
        return [word for word in tweet if word not in self._stopwords]


tweetProcessor = PreProcessTweets()
preprocessedTestSet = tweetProcessor.processTweets(testDataSet)
tweetProcessor = PreProcessTweets()
print(preprocessedTestSet)

希望它能帮上忙！