https://github.com/harmanveer-2546/tweets-cleaning-with-python

Twitter is one of the most used data sources for data analysis. The reason is that it’s open and free to collect unless you subscribe to the paid version one. Besides, it’s pretty simple to collect data from it.
https://github.com/harmanveer-2546/tweets-cleaning-with-python

filtering function-calling nlptk numpy os pandas punctuation python re removing-links tokenization tweet-cleaning twitter-data-analysis twitter-sentiment-analysis

Last synced: 6 days ago
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Twitter is one of the most used data sources for data analysis. The reason is that it’s open and free to collect unless you subscribe to the paid version one. Besides, it’s pretty simple to collect data from it.

• Host: GitHub
• URL: https://github.com/harmanveer-2546/tweets-cleaning-with-python
• Owner: harmanveer-2546
• Created: 2024-07-13T18:38:01.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-07-13T19:05:58.000Z (4 months ago)
• Last Synced: 2024-07-13T20:24:24.505Z (4 months ago)
• Topics: filtering, function-calling, nlptk, numpy, os, pandas, punctuation, python, re, removing-links, tokenization, tweet-cleaning, twitter-data-analysis, twitter-sentiment-analysis
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/datasets/tariqsays/sentiment-dataset-with-1-million-tweets
• Size: 9.77 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Tweets Cleaning with Python

Twitter data contains a bunch of information parameters. Sometimes, the data contain unnecessary things that need to be cleaned, such as unnecessary characters, links, newlines, and other kinds of stuff. In this article, I’m going to show you how to clean Twitter data using the python programming language.