This Python project offers an automated scraper specifically designed to collect and analyze public data from IBM posts on LinkedIn. It utilizes Selenium for web scraping and empowers data-driven decision making for IBM's social media strategy on the platform.
Efficiently scrapes public data from IBM posts on LinkedIn. Extracts valuable information, including engagement metrics (reactions, comments, reposts), content categories (images, videos, articles, documents), follower count by year, and potentially post edit status. Facilitates data analysis to understand user interaction with IBM's content, content performance based on format, follower growth, and the potential impact of post editing. Provides a foundation for further development, such as expanding data collection, sentiment analysis, and machine learning integration.
- Python 3.x (https://www.python.org/downloads/)
- Selenium (https://pypi.org/project/selenium/)
- Pandas
- Seaborn
The scraped data will be stored in a structured format, such as a CSV file.