Big Data in Social Media Marketing: Analyzing Consumer Sentiment and Engagement Patterns

Abstract

Big Data analytics has emerged as a critical tool in understanding consumer sentiment and engagement patterns, particularly within the fast-paced environment of social media marketing. Analyzing massive amounts of user-generated content—tweets, posts, comments, and reviews—enables businesses to capture nuanced insights into consumer preferences, intentions, and emotional states. This paper provides a technical exploration of how advanced data collection, preprocessing, and modeling methodologies can be leveraged to distill meaningful, actionable information from large-scale social media data streams. We address the complexities of processing heterogeneous data sources, including textual, visual, and behavioral signals, while discussing both classical and deep learning-based sentiment analysis frameworks. Moreover, we investigate how modeling engagement patterns—including likes, shares, and comments—can reveal network effects and viral propagation processes crucial to marketing success. Throughout, we integrate linear algebraic tools, such as matrix factorization and vector-based embeddings, highlighting their relevance in feature extraction and dimensionality reduction. The end goal is to outline a robust end-to-end pipeline—from data ingestion to interpretative modeling—that can guide the design of effective marketing strategies. By uniting statistical rigor with modern computational techniques, this paper underscores the pivotal role of Big Data in enabling precise targeting, real-time consumer feedback, and ultimately more effective social media marketing campaigns.