Analyzing Social Media Engagement Metrics and Sentiment Trends for Enhanced Campaign Strategies.

Social media has become a vital platform for businesses, organizations, and individuals to engage with audiences. Understanding the dynamics of engagement is crucial for optimizing campaigns and enhancing outreach. This study analyzes two datasets—one in Excel format and one in CSV containing posts, engagement metrics (likes, shares, comments, impressions, engagement rate), post types, campaigns, and sentiment labels. Using Python-based analysis, we examine temporal patterns, platform variations, and sentiment relationships. The findings reveal that engagement distribution is skewed, with a small number of posts driving the majority of interactions. Time-of-day and campaign-specific factors influence engagement, while sentiment shows a weak correlation with measurable metrics. These insights provide a foundation for actionable strategies such as content optimization, campaign-level analysis, and A/B testing of posting times.