kennedyngigi4/facebook-posts-scraping-and-analysis

Scraping and analyzing Facebook posts

Data analysis has become a key function within companies. Most companies are relying on analysis to make informed decisions and also predict the future. To be able to make such decisions they need to have large datasets that relates or affects their business models.

Datasets can be gotten through various methods eg;

• Scraping
• Conducting surveys.
• Interviews
• Sampling
• Research
• etc

Scraping

This is the method of pulling information from websites into your most preferred file, database etc. The method is an efficient way to grab a great deal of information for analysis, processing, or presentation.

Scraping of certain platforms such as social media platforms can help a company make informed decisions from users reactions, comments and shares.

Example A person opening a restaurant in a certain area can check Facebook pages of competitors within that location. Get helpful insights from the reactions, comments and shares of their followers. From the insight developed the new restaurant can decide to;

1. Offer a discount to any meal that most people like or have a positive feedback
2. Improve on quality of certain meals to get more customers
3. Use different flavors/recipes to make other delicious products
4. etc

In this case I am going to scrap posts from a facebook page and tend to draw some insight from the posts made on that page.

Tools used

Facebook scraper - Python library built to help in scrapping posts (Has BeautifulSoup in it already) Pandas - Used for creating and working with dataframes and series of dataset obtained Matplotlib/ Seaborn - For plots Azure text analytics - Sentiment analysis and also key phrase extraction