用大模型分析印地语混合评论中的讽刺，提升低资源语言情感识别效果。
YouTube Comments Decoded: Leveraging LLMs for Low Resource Language Classification
- 用GPT-3.5 Turbo提示词进行讽刺分类，适配泰米尔-英语和马拉雅拉姆-英语混用文本。
- 泰米尔语讽刺识别宏平均F1达0.61，马拉雅拉姆语为0.50。
- 针对真实社交平台数据构建标注语料，解决低资源语言下的情感偏斜问题。
讽刺检测是情感分析中的重大挑战，尤其在社交媒体中，由于代码混用现象普遍，尤其是德拉威语系语言中表现突出。代码混用指单条语句中混合多种语言，常伴有非母语书写系统，使依赖单一语言训练的系统难以应对。本任务提出一个新型黄金标准语料库，用于泰米尔-英语与马拉雅拉姆-英语混合文本中的讽刺与情感识别。目标是在社交媒体收集的泰米尔-英语和马拉雅拉姆-英语评论与帖子中，识别讽刺及情感极性。每条消息均在语义层面标注情感极性，特别关注现实场景中的类别不平衡问题。本文通过提示工程使用GPT-3.5 Turbo对评论进行讽刺分类，获得泰米尔语宏平均F1为0.61，马拉雅拉姆语为0.50。
原文摘要 · Abstract (English)
Sarcasm detection is a significant challenge in sentiment analysis, particularly due to its nature of conveying opinions where the intended meaning deviates from the literal expression. This challenge is heightened in social media contexts where code-mixing, especially in Dravidian languages, is prevalent. Code-mixing involves the blending of multiple languages within a single utterance, often with non-native scripts, complicating the task for systems trained on monolingual data. This shared task introduces a novel gold standard corpus designed for sarcasm and sentiment detection within code-mixed texts, specifically in Tamil-English and Malayalam-English languages. The primary objective of this task is to identify sarcasm and sentiment polarity within a code-mixed dataset of Tamil-English and Malayalam-English comments and posts collected from social media platforms. Each comment or post is annotated at the message level for sentiment polarity, with particular attention to the challenges posed by class imbalance, reflecting real-world scenarios.In this work, we experiment with state-of-the-art large language models like GPT-3.5 Turbo via prompting to classify comments into sarcastic or non-sarcastic categories. We obtained a macro-F1 score of 0.61 for Tamil language. We obtained a macro-F1 score of 0.50 for Malayalam language.
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