A Comparative Evaluation of Deep Learning and Rule-Based Models for Sentiment Analysis of 5G/6G Public Discourse on Social Media

Next-generation communication technologies are increasingly shaping not only network infrastructure and digital services, but also public expectations, risk perceptions, and policy debates. As 5G deployment continues and 6G research accelerates, social responses to communication technologies have arguably become an important dimension of technology adoption, governance, and regulatory decision-making. Social media platforms provide timely and large-scale data sources for public opinion analysis. However, 5G/6G-related discourse often contains domain-specific terminology, technical complaints, and complex emotional expressions, which pose challenges for sentiment analysis. To address this challenge, this study constructs a manually annotated dataset of 1746 5G/6G-related Twitter posts collected across multiple communication-related events. This study aims to provide a domain-specific empirical evaluation of sentiment analysis models by examining classification performance, deployment-oriented inference efficiency, and lightweight domain adaptation. Three sentiment analysis methods are evaluated: twitter–roberta–base–sentiment, bertweet–base–sentiment–analysis, and VADER. In addition, a filtered Amazon Reviews’23 subset is used as an external review-style dataset, and a LoRA-based fine-tuning experiment is performed on Twitter-RoBERTa to examine domain adaptability. The results show that pre-trained language models achieve stronger classification performance than the rule-based method, particularly for domain-specific and semantically complex texts. VADER, by contrast, shows high observed efficiency under its CPU-based deployment setting, especially for short-text inference. The LoRA fine-tuned RoBERTa model further improves classification performance on both Twitter and Amazon test sets, indicating that lightweight parameter-efficient adaptation can enhance model robustness in specialized 5G/6G discourse. These findings contribute a domain-specific dataset, a deployment-oriented comparison of sentiment analysis paradigms, and empirical evidence on lightweight domain adaptation for 5G/6G-related public opinion monitoring.