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.

翻译：讽刺检测是情感分析中的一项重大挑战，这尤其源于其表达观点时真实意图偏离字面含义的特性。在社交媒体语境中，这一挑战因语码混合现象（尤其在达罗毗荼语系语言中）的普遍存在而加剧。语码混合指在单个话语中混用多种语言，常涉及非母语文字，这使得基于单语数据训练的系统处理该任务变得复杂。本共享任务引入了一个新颖的黄金标准语料库，专为语码混合文本（具体为泰米尔语-英语和马拉雅拉姆语-英语）中的讽刺与情感检测而设计。该任务的主要目标是从社交媒体平台收集的泰米尔语-英语和马拉雅拉姆语-英语评论及帖子构成的语码混合数据集中，识别讽刺内容与情感极性。每条评论或帖子均在消息级别标注了情感极性，并特别关注了类别不平衡带来的挑战，以反映真实场景。在本研究中，我们通过提示工程，尝试使用GPT-3.5 Turbo等先进大语言模型将评论分类为讽刺性或非讽刺性类别。对于泰米尔语，我们获得了0.61的宏观F1分数；对于马拉雅拉姆语，我们获得了0.50的宏观F1分数。