Scientific Trends in Big Data Analytics for Sentiment Insight: A Bibliometric Study in Digital Marketing

This study investigates how big data–driven sentiment analysis has evolved within the digital marketing domain over the past decade. The research is guided by the question of how scholarly production, methodological advancements, and global contributions have shaped the field from 2015 to 2025. It is hypothesized that the rapid adoption of deep learning, multimodal analytics, and transformer-based architectures has significantly influenced scientific growth and thematic direction. To examine this, a bibliometric method was employed using Scopus as the primary data source, yielding 169 documents after applying filters on timespan, subject area, and document type. The analysis used performance indicators and science mapping techniques with Bibliometrix and Biblioshiny. Results reveal a substantial annual growth rate of 34.47%, driven by increasing reliance on AI-enabled analytics in marketing strategy, consumer insight extraction, and digital engagement. Country-level patterns show Asia as a leading productivity hub, while Western countries produce the most influential works. The findings highlight critical shifts in research focus and underscore the strategic importance of sentiment analysis for future intelligent marketing systems. These results offer valuable implications for scholars and practitioners seeking to align with technological disruptions and evolving data-driven ecosystems.