用MARBERT预测阿拉伯语推文表情符号，准确率达75%
Machine learning and emoji prediction: How much accuracy can MARBERT achieve?
- 基于14类表情符号标签微调MARBERT模型
- 整体准确率75%，多方言场景下表现良好
- 适合关注低资源语言与社交文本生成的研究者
本研究探讨了机器学习在阿拉伯语推文表情符号预测中的应用，采用先进的MARBERT模型。通过Python从X.com收集了11379条涵盖多种阿拉伯口语方言的推文，最终筛选出8695条用于分析。这些推文被划分为14个类别，并以数值编码作为标签。设计了一套可解释的预处理流程，用于分析词汇特征与表情符号类别间的关系。对MARBERT进行微调以实现从文本输入预测表情符号。评估指标包括精确率、召回率和F1分数。结果显示，模型整体准确率达到0.75。研究认为尽管结果令人鼓舞，但针对阿拉伯语等低资源、多方言语言，仍需进一步提升机器学习模型性能。
原文摘要 · Abstract (English)
This study investigates Machine Learning (ML) in the prediction of emojis in Arabic tweets employing the (state-of-the-art) MARBERT model. A corpus of 11379 CA tweets representing multiple Arabic colloquial dialects was collected from X.com via Python. A net dataset includes 8695 tweets, which were utilized for the analysis. These tweets were then classified into 14 categories, which were numerically encoded and used as labels. A preprocessing pipeline was designed as an interpretable baseline, allowing us to examine the relationship between lexical features and emoji categories. MARBERT was finetuned to predict emoji use from textual input. We evaluated the model performance in terms of precision, recall and F1-scores. Findings reveal that the model performed quite well with an overall accuracy 0.75. The study concludes that although the findings are promising, there is still a need for improving machine learning models including MARBERT, specifically for low-resource and multidialectal languages like Arabic.
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