机器学习与数字语用学：哪类词对表情符号使用的影响最大？
Machine learning and digital pragmatics: Which word category influences emoji use most?
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中文总结 AI 辅助
本研究采用数字语用学方法，通过MARBERT模型与二元逻辑回归分析X平台口语阿拉伯语语料，发现动词对表情符号使用的影响强于名词，相关关联可通过多方法混合路径解释。
中文摘要 AI 辅助
本研究采用数字语用学方法（DPA），探究最先进的MARBERT模型在识别X平台上与表情符号使用相关的词汇/语用类别的性能。使用Python从X平台收集了包含表情符号的15856条口语阿拉伯语（CA）帖子构成的净语料库，将文本进行分词并归一化为4个词汇类别，即名词归一化（noun_norm）、动词归一化（verb_norm）、形容词归一化（adj_norm）、副词归一化（adverb_norm），以及2个语用/结构类别：疑问归一化（question_norm）和感叹归一化（exclamation_norm）。对MARBERT进行微调与优化，以识别哪个类别在标准指标上得分更高，因此与表情符号使用相关；同时采用二元逻辑回归检验哪个类别在统计上与表情符号出现相关。研究结果显示，名词在语料库归一化频率中占主导地位（M=0.675，SD=0.161），其次是动词（M=0.083，SD=0.100）；但动词通过动词密度表现出对表情符号使用的最强影响（β=0.821，p=0.001，95%置信区间[0.332,1.309]）。本研究得出结论：在X平台上的口语阿拉伯语数字语用学中，表情符号使用与词汇/语用类别的关联可通过计算、统计和语用方法的混合方法解释，反映了机器学习、语言/词汇特征、语境表征与语用交际之间的相互作用。
英文摘要
This study examines the performance of the state-of-the-art MARBERT model in identifying the lexical/pragmatic category associated with emoji use on X within a digital pragmatics approach (DPA). A net corpus of 15856 Colloquial Arabic (CA) posts containing emojis was collected from X using Python. The texts were tokenized and normalized into 4 lexical categories, namely noun_norm, verb_norm, adj_norm, and adverb_norm, and 2 pragmatic/structural categories, question_norm and exclamation_norm. MARBERT was finetuned and optimized to identify which category scores standard metrics more, hence associated with emoji use, while binary logistic regression was used to examine which category is statistically associated with emoji occurrence. Findings unveil that nouns dominate the corpus in normalized frequency (M = 0.675, SD = 0.161), followed by verbs (M = 0.083, SD = 0.100). However, verbs have the strongest influence of emoji use indicated by verb density (\b{eta} = 0.821, p = .001, 95% CI [0.332, 1.309]). The study concludes that in digital pragmatics of CA on X, emoji use association with lexical/pragmatic category can be explained by a hybrid approach of computational, statistical, and pragmatic methods, reflecting the interaction among machine learning, linguistic/lexical features, contextual representation, and pragmatic communication.
