This study presents a thorough examination of various Generative Pretrained Transformer (GPT) methodologies in sentiment analysis, specifically in the context of Task 4 on the SemEval 2017 dataset. Three primary strategies are employed: 1) prompt engineering using the advanced GPT-3.5 Turbo, 2) fine-tuning GPT models, and 3) an inventive approach to embedding classification. The research yields detailed comparative insights among these strategies and individual GPT models, revealing their unique strengths and potential limitations. Additionally, the study compares these GPT-based methodologies with other current, high-performing models previously used with the same dataset. The results illustrate the significant superiority of the GPT approaches in terms of predictive performance, more than 22\% in F1-score compared to the state-of-the-art. Further, the paper sheds light on common challenges in sentiment analysis tasks, such as understanding context and detecting sarcasm. It underscores the enhanced capabilities of the GPT models to effectively handle these complexities. Taken together, these findings highlight the promising potential of GPT models in sentiment analysis, setting the stage for future research in this field. The code can be found at https://github.com/DSAatUSU/SentimentGPT

翻译：本研究系统考察了多种生成式预训练Transformer（GPT）方法在情感分析中的应用，具体针对SemEval 2017数据集上的任务4展开。研究采用了三种主要策略：1）使用高级GPT-3.5 Turbo进行提示工程，2）微调GPT模型，3）一种创新的嵌入分类方法。研究揭示了这些策略及不同GPT模型间的详细对比洞察，展现了其独特优势与潜在局限。此外，研究将基于GPT的方法与先前在同一数据集上使用的其他高性能模型进行了比较。结果表明，GPT方法在预测性能上具有显著优势，F1分数相比现有最优方法提升超过22%。更进一步，本文揭示了情感分析任务中的常见挑战，如上下文理解与反讽检测，并强调了GPT模型在有效应对这些复杂性方面的增强能力。综合而言，这些发现凸显了GPT模型在情感分析领域的巨大潜力，为未来该领域的研究奠定基础。代码详见https://github.com/DSAatUSU/SentimentGPT