Title: Sentiment Analysis of Spanish Political Party Communications on Twitter Using Pre-trained Language Models Authors: Chuqiao Song, Shunzhang Chen, Xinyi Cai, Hao Chen Comments: 21 pages, 6 figures Abstract: This study investigates sentiment patterns within Spanish political party communications on Twitter by leveraging BETO and RoBERTuito, two pre-trained language models optimized for Spanish text. Using a dataset of tweets from major Spanish political parties: PSOE, PP, Vox, Podemos, and Ciudadanos, spanning 2019 to 2024, this research analyzes sentiment distributions and explores the relationship between sentiment expression and party ideology. The findings indicate that both models consistently identify a predominant Neutral sentiment across all parties, with significant variations in Negative and Positive sentiments that align with ideological distinctions. Specifically, Vox exhibits higher levels of Negative sentiment, while PSOE demonstrates relatively high Positive sentiment, supporting the hypothesis that emotional appeals in political messaging reflect ideological stances. This study underscores the potential of pre-trained language models for non-English sentiment analysis on social media, providing insights into sentiment dynamics that shape public discourse within Spain's multi-party political system. Keywords: Spanish politics, sentiment analysis, pre-trained language models, Twitter, BETO, RoBERTuito, political ideology, multi-party system

翻译：本研究利用针对西班牙语文本优化的两种预训练语言模型BETO与RoBERTuito，探究西班牙政党在Twitter平台传播内容中的情感模式。通过分析西班牙主要政党（包括PSOE、PP、Vox、Podemos及Ciudadanos）在2019年至2024年间发布的推文数据集，本研究系统考察了情感分布特征，并深入探讨情感表达与政党意识形态之间的关联。研究结果表明：两种模型均一致检测到所有政党推文中占据主导的中性情感，而消极与积极情感的显著差异则与意识形态分野呈现对应关系。具体而言，Vox表现出更高程度的消极情感，而PSOE则显示出相对较高的积极情感，这支持了政治传播中情感诉求反映意识形态立场的假设。本研究凸显了预训练语言模型在非英语社交媒体情感分析中的应用潜力，为理解西班牙多党制政治体系中塑造公共话语的情感动态提供了新的见解。关键词：西班牙政治，情感分析，预训练语言模型，Twitter，BETO，RoBERTuito，政治意识形态，多党制