[英]TypeError: string indices must be integers (Text Data Preprocessing in CSV files for Sentiment Analysis)
我对编程和NLP有点新意。 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 )用于情绪分析在推特上。 我有我需要的csv文件,因此我只是通过文件定义变量而不是构建它们。
当我尝试运行代码时,它在运行此行时给出了类型错误:
preprocessedTrainingSet = tweetProcessor.processTweets(trainingData)
并追溯到这条线:
processedTweets.append((self._processTweet(鸣叫[ “文本”]),鸣叫[ “标签”]))。
我不知道如何规避问题,仍然保持代码的核心功能完好无损。
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)
我希望它能开始清理我在开始使用朴素贝叶斯之前找到的数据
没有你的实际数据很难说,但我认为你们互相混淆了多种类型。
我从这个网站下载了一些推文。 通过这些数据,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)
希望能帮助到你!
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