Social Media Analysis

• Problem Statement: Social media platforms generate vast amounts of textual data in the form of posts, comments, and messages. Understanding the relationships between topics, users, and engagement metrics is crucial for social media marketers and analysts;
• Application of ARM: Association rule mining can be used to analyze social media data to uncover patterns and associations between topics, hashtags, user interactions, and user demographics;
• Example: Suppose we have a dataset of Twitter posts and associated hashtags. By applying ARM techniques, we can identify frequent co-occurring hashtags, uncovering implicit relationships between topics. For example, we might discover that posts containing #travel often also include #wanderlust and #vacation, suggesting related interests among users.

Customer Support Ticket Analysis

• Problem Statement: Customer support departments receive a large volume of tickets or inquiries, each containing various keywords or issues. Identifying common themes or patterns in support tickets can help streamline support processes and improve customer satisfaction;
• Application of ARM: Association rule mining can be applied to analyze support ticket data to identify frequent combinations of issues, keywords, or customer feedback sentiments;
• Example: Consider a dataset of customer support tickets from an e-commerce platform. By applying ARM, we can discover associations between keywords like "delivery delay," "refund request," and "product damaged." This analysis can help prioritize support efforts, automate responses to common issues, and identify opportunities for process improvement.