Sentiment and Digital Discourse Analysis of Sukuk on X: Text Mining of Dominant Themes and Public Perception

Abstract

Introduction/Main Objectives: This Study Examines Public Discourse On Sukuk On X (Formerly Twitter) Using Text Mining And Sentiment Analysis To Identify Dominant Themes And Public Perceptions. Research Methods: A Total Of 452 Sukuk-Related Posts Collected From 4 August To 30 September 2025 Were Analyzed Using Orange Data Mining For Preprocessing, Tokenization, Sentiment Classification, Keyword Extraction, And Visualization. Finding/Results: Neutral Sentiment Dominated Sukuk Discourse, Followed By Positive Sentiment, While Negative Sentiment Was Limited. English-Language Posts Were More Positive, Whereas Indonesian Posts Were Mostly Informational And Neutral. Dominant Themes Included Investment, State Financing, Sharia Principles, Green Finance, And Indonesia’s Role In Global Islamic Finance. Conclusion: The Study Extends Islamic Finance Research By Applying Sentiment And Digital Discourse Analysis To Sukuk-Related Social Media Discussions.