A Multi-Layered Analysis of Digital Public Discourse on Electric Vehicles: Integrating Sentiment, Emotion, and Network Structures in Social Media - Riki Abu Brantas; Asep Miftahuddin; Eka Surachman; Budhi Pamungkas Gautama; Yoga Perdana; Arciana Damayanti (Apollo: Journal of Tourism and Business, 2026)
This study analyses the structure and dynamics of digital public discourse on electric vehicles using a data-driven social media analytics approach. A total of 219 tweets were collected through the SocialX data mining system and analysed using Natural Language Processing (NLP), Social Network Analysis (SNA), Text Network Analysis (TNA), sentiment analysis, emotion analysis, trend analysis, and zero-shot classification. The analytical process included word cloud extraction, transformer-based sentiment and emotion classification, network mapping, and temporal trend analysis. The results indicate that electric vehicle discourse is highly dynamic and event-driven, with discussion peaks influenced by policy developments and technological innovations. Sentiment analysis shows a predominance of neutral content, suggesting that users mainly engage in information sharing, while positive and negative sentiments reflect support and criticism. Emotion analysis reveals optimism as the dominant emotion, accompanied by concern and critical perspectives. TNA identifies interconnected themes related to electricity, vehicles, policy, and economic issues, whereas SNA highlights a hybrid interaction structure in which several influential actors play key roles in information dissemination and discourse amplification. This study contributes an integrated analytical framework that combines textual, emotional, structural, and temporal dimensions of online discourse. The findings provide valuable insights for policymakers, industry stakeholders, and communication practitioners in developing more effective electric vehicle communication strategies and policies.