AI 中文总结
该研究探究基于Transformer的模型学习阿拉伯语数字话语中表情符号语用学的能力，通过对MARBERT微调建模5种人际语用功能，验证其能捕捉相关人际功能，为表情符号语用学建模提供新计算方法并推动相关领域融合。
AI 中文摘要
本研究探究基于Transformer的模型学习阿拉伯语数字话语（ADD）中表情符号语用学的能力，提供MARBERT在人际语用功能（IPFs）方面的行为证据。研究使用通过Python从Facebook收集的8504条独特表情符号帖子构成的语料库，对这些帖子进行手动标注，开发并标记了礼貌、尊重、团结、共情、鼓励这5种IPFs。采用混合方法，包含统计方法及结合言语行为理论、礼貌理论和关系管理理论的解释性分析。对MARBERT进行微调以建模这些依赖语境的语用功能。研究结果表明MARBERT具备学习这些IPFs的能力，在未见过的数据上表现出色，准确率达93%，微F1分数为0.61，宏F1分数为0.56，证明其在捕捉超越传统情感分析的人际功能方面的有效性。功能级评估显示，礼貌和尊重的识别准确率高于团结，反映出IPFs在明确性和语境依赖性上的差异。研究结论指出，基于Transformer的模型能学习面子管理和关系沟通的模式，但在处理高度隐含的社会意义时仍面临挑战。本研究为表情符号语用学建模提供了一种新颖的计算方法，推动了人际语用学与自然语言处理（NLP）在数字传播研究中的融合。
英文摘要
This study examines Transformer-based models' ability to learn emoji pragmatics in Arabic digital discourse (ADD), providing evidence from MARBERT's behavior with interpersonal pragmatic functions (IPFs). A corpus of 8,504 unique emoji-posts collected from Facebook via Python was used in the study. These posts were manually annotated, developed, and labeled for five IPFs: Politeness, Respect, Solidarity, Empathy, and Encouragement. A mixed-method approach was employed comprising statistical methods and interpretative analyses involving speech act theory, politeness theory, and rapport management theory. MARBERT was fine-tuned to model these context-dependent pragmatic functions. Findings demonstrate MARBERT's ability to learn these IPFs, achieving strong performance on unseen data, with an accuracy of 93%, a micro F1-score of 0.61, and a macro F1-score of 0.56, demonstrating its effectiveness in capturing interpersonal functions beyond conventional sentiment analysis. Function-level evaluation showed that Politeness and Respect were identified more accurately than Solidarity, reflecting differences in the explicitness and contextual dependence of IPFs. The study concludes that Transformer-based models learn patterns of face management and relational communication but remain challenged by highly implicit social meanings. It contributes a novel computational approach to modeling emoji pragmatics and advances the integration of interpersonal pragmatics with NLP for digital communication research.
Commentspages 22, tables 3, figure 4
