使用Python提取包含特定主题标签的推文
Twitter 是最受欢迎的社交媒体平台之一。 Twitter API 提供了你需要的工具来参与、参与和分析 Twitter 上发生的对话，它在数据分析和人工智能等领域有很多应用。本文重点介绍如何从给定日期开始提取具有特定 Hashtag 的推文。
要求：
- Tweepy是一个Python包，用于轻松访问 Twitter API。 Twitter API 提供的几乎所有功能都可以通过Tweepy使用。要安装此类型，请在终端中输入以下命令。
pip install Tweepy
- Pandas是一个非常强大的Python数据分析框架。它建立在Numpy Package 之上，其关键数据结构是一个DataFrame ，可以在其中操作表格数据。要安装此类型，请在终端中输入以下命令。
pip install pandas
先决条件：
- 创建一个 Twitter 开发者帐户并获取您的消费者密钥和访问令牌
- 通过在命令提示符中运行此命令在您的系统上安装Tweepy和Pandas模块
循序渐进的方法：
- 导入所需的模块。
- 创建一个显式函数来显示推文数据。
- 创建另一个函数来使用tweepy模块抓取有关给定 Hashtag 的数据。
- 在驱动程序代码中，分配 Twitter 开发人员帐户凭据以及标签、初始日期和推文数量。
- 最后，调用函数以 Hashtag、初始日期和推文数量作为参数来抓取数据。
以下是基于上述方法的完整程序：
Python
# Python Script to Extract tweets of a
# particular Hashtag using Tweepy and Pandas
# import modules
import pandas as pd
import tweepy
# function to display data of each tweet
def printtweetdata(n, ith_tweet):
print()
print(f"Tweet {n}:")
print(f"Username:{ith_tweet[0]}")
print(f"Description:{ith_tweet[1]}")
print(f"Location:{ith_tweet[2]}")
print(f"Following Count:{ith_tweet[3]}")
print(f"Follower Count:{ith_tweet[4]}")
print(f"Total Tweets:{ith_tweet[5]}")
print(f"Retweet Count:{ith_tweet[6]}")
print(f"Tweet Text:{ith_tweet[7]}")
print(f"Hashtags Used:{ith_tweet[8]}")
# function to perform data extraction
def scrape(words, date_since, numtweet):
# Creating DataFrame using pandas
db = pd.DataFrame(columns=['username', 'description', 'location', 'following',
'followers', 'totaltweets', 'retweetcount', 'text', 'hashtags'])
# We are using .Cursor() to search through twitter for the required tweets.
# The number of tweets can be restricted using .items(number of tweets)
tweets = tweepy.Cursor(api.search, q=words, lang="en",
since=date_since, tweet_mode='extended').items(numtweet)
# .Cursor() returns an iterable object. Each item in
# the iterator has various attributes that you can access to
# get information about each tweet
list_tweets = [tweet for tweet in tweets]
# Counter to maintain Tweet Count
i = 1
# we will iterate over each tweet in the list for extracting information about each tweet
for tweet in list_tweets:
username = tweet.user.screen_name
description = tweet.user.description
location = tweet.user.location
following = tweet.user.friends_count
followers = tweet.user.followers_count
totaltweets = tweet.user.statuses_count
retweetcount = tweet.retweet_count
hashtags = tweet.entities['hashtags']
# Retweets can be distinguished by a retweeted_status attribute,
# in case it is an invalid reference, except block will be executed
try:
text = tweet.retweeted_status.full_text
except AttributeError:
text = tweet.full_text
hashtext = list()
for j in range(0, len(hashtags)):
hashtext.append(hashtags[j]['text'])
# Here we are appending all the extracted information in the DataFrame
ith_tweet = [username, description, location, following,
followers, totaltweets, retweetcount, text, hashtext]
db.loc[len(db)] = ith_tweet
# Function call to print tweet data on screen
printtweetdata(i, ith_tweet)
i = i+1
filename = 'scraped_tweets.csv'
# we will save our database as a CSV file.
db.to_csv(filename)
if __name__ == '__main__':
# Enter your own credentials obtained
# from your developer account
consumer_key = "XXXXXXXXXXXXXXXXXXXXX"
consumer_secret = "XXXXXXXXXXXXXXXXXXXXX"
access_key = "XXXXXXXXXXXXXXXXXXXXX"
access_secret = "XXXXXXXXXXXXXXXXXXXXX"
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_key, access_secret)
api = tweepy.API(auth)
# Enter Hashtag and initial date
print("Enter Twitter HashTag to search for")
words = input()
print("Enter Date since The Tweets are required in yyyy-mm--dd")
date_since = input()
# number of tweets you want to extract in one run
numtweet = 100
scrape(words, date_since, numtweet)
print('Scraping has completed!')输出：
演示：