用AI分析阿拉伯语推文，发现动词最影响表情符号使用
Machine learning and digital pragmatics: Which word category influences emoji use most?
- 用MARBERT模型分析1.5万条阿拉伯语推文的词类与表情符号关系
- 动词密度与表情符号使用强相关（η = 0.821，p < 0.001）
- 适合对社交媒体语言、计算语用学感兴趣的读者
本研究基于数字语用学方法（DPA），考察最先进的MARBERT模型在识别推特（X）上与表情符号使用相关的词汇/语用类别中的表现。通过Python收集了15,856条包含表情符号的口语阿拉伯语（CA）推文，经分词与标准化后分为4类词汇类别：名词_norm、动词_norm、形容词_norm、副词_norm，以及2类语用/结构类别：疑问句_norm和感叹句_norm。对MARBERT进行微调与优化，以识别哪类在标准指标上得分更高，从而关联表情符号使用；同时采用二元逻辑回归分析哪类在统计上与表情符号出现相关。结果表明，名词在归一化频率中占主导地位（M = 0.675，SD = 0.161），其次为动词（M = 0.083，SD = 0.100）。然而，动词密度与表情符号使用最强相关（η = 0.821，p = .001，95% CI [0.332, 1.309]）。研究结论指出，在阿拉伯语推特的数字语用学中，表情符号使用与词汇/语用类别的关联可通过计算、统计与语用方法的混合范式解释，体现机器学习、语言特征、上下文表征与语用交流之间的互动。
原文摘要 · Abstract (English)
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.
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