From Words to Networks: A Bibliometric Exploration of Co-authorship, Co-citation, and Keyword Trends

Abstract

Purpose: This study aims to map the research landscape of sentiment analysis by examining authorship patterns, intellectual structure, and emerging thematic trends.

Design/Methodology/Approach: A bibliometric analysis was conducted using 2,697 publications retrieved from the Scopus database for the period 2004–2025. Data were analysed using co-authorship, co-citation, and keyword co-occurrence techniques, and visualised through bibliometric mapping tools.

Findings: The results indicate that China and India lead in research productivity, while the United States dominates in citation impact. Influential authors such as Bing Liu and Erik Cambria shape the intellectual structure of the field. Keyword analysis highlights the dominance of sentiment analysis, social media, and data mining, with a clear shift toward machine learning and deep learning approaches. Co-citation analysis reveals three major research clusters: traditional methods, lexicon-based approaches, and AI-driven techniques.

Conclusion: The study demonstrates the rapid growth and increasing interdisciplinary of sentiment analysis, highlighting its evolving methodologies and expanding applications across diverse domains.