Everything clean
Back to your Twitter sentiment analysis project! There are several types of strings that increase your sentiment analysis complexity. But these strings do not provide any useful sentiment. Among them, we can have links and user mentions.
In order to clean the tweets, you want to extract some examples first. You know that most of the times links start with http and do not contain any whitespace, e.g. https://www.datacamp.com. User mentions start with @ and can have letters and numbers only, e.g. @johnsmith3.
You write down some helpful quantifiers to help you: * zero or more times, + once or more, ? zero or once.
The list sentiment_analysis containing the text of three tweets are already loaded in your session. You can use print() to view the data in the IPython Shell.
이 연습은 강의의 일부입니다
Regular Expressions in Python
연습 안내
- Import the
remodule. - Write a regex to find all the matches of
httplinks appearing in eachtweetinsentiment_analysis. Print out the result. - Write a regex to find all the matches of user mentions appearing in each
tweetinsentiment_analysis. Print out the result.
실습형 인터랙티브 연습
이 예제를 이 샘플 코드를 완성하여 풀어보세요.
# Import re module
____
for tweet in sentiment_analysis:
# Write regex to match http links and print out result
print(re.____(____"____", ____))
# Write regex to match user mentions and print out result
print(re.____(____"____", ____))