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Tweets-Scrapper

A Python-based tool for scraping tweets related to specific keywords, analyzing their sentiment, and saving the results to a CSV file.

Dependencies Installation

To get started, you'll need to install the required dependencies. Use pip to install all dependencies from the requirements.txt file:

pip install -r requirements.txt

Usage

To use the scraper, download the project from GitHub (click the green "Code" button and then "Download ZIP"). After extracting the files, you can easily parse a specific Twitter keyword by running the following Python code:

>>> from twitter.tweety import tweets
>>> tweety = tweets('$BTC')  # Replace '$BTC' with any keyword you're interested in

Output

The scraper will generate a CSV file containing the following information for each tweet:

• tweet: The text of the tweet
• time: Timestamp of the tweet
• sentiment: Sentiment polarity (positive, neutral, or negative)
• vader_compound: Compound score calculated by VADER sentiment analysis
• vader_neg: VADER negative sentiment score
• vader_neu: VADER neutral sentiment score
• vader_pos: VADER positive sentiment score

Sample CSV Format:

tweet	time	sentiment	vader_compound	vader_neg	vader_neu	vader_pos
Bitcoin is skyrocketing! #BTC	2025-02-03 12:34:56	Positive	0.75	0.10	0.20	0.70
Cryptocurrency market is volatile...	2025-02-03 12:45:01	Negative	-0.60	0.30	0.60	0.10
The future of digital assets is here.	2025-02-03 13:01:45	Neutral	0.05	0.05	0.90	0.05

Tested Using

• Chromium version: 132.0.6834.159
• Platform: Debian GNU/Linux 12 (Bookworm)

License

This project is licensed under the MIT License. See the LICENSE file for details.