Opinion mining, also known as sentiment analysis, is a subfield of natural language processing (NLP) that focuses on identifying and extracting subjective information in textual material. This can include determining the overall sentiment of a piece of text (e.g., positive or negative), as well as identifying specific emotions or opinions expressed in the text, that involves the use of advanced machine and deep learning techniques. Recently, transformer-based language models make this task of human emotion analysis intuitive, thanks to the attention mechanism and parallel computation. These advantages make such models very powerful on linguistic tasks, unlike recurrent neural networks that spend a lot of time on sequential processing, making them prone to fail when it comes to processing long text. The scope of our paper aims to study the behaviour of the cutting-edge Transformer-based language models on opinion mining and provide a high-level comparison between them to highlight their key particularities. Additionally, our comparative study shows leads and paves the way for production engineers regarding the approach to focus on and is useful for researchers as it provides guidelines for future research subjects.

翻译：观点挖掘（又称情感分析）是自然语言处理（NLP）的一个子领域，专注于识别和提取文本材料中的主观信息。这包括确定文本的整体情感倾向（如正面或负面），以及识别文本中表达的具体情绪或观点。该领域涉及先进的机器学习和深度学习技术。近年来，基于Transformer的语言模型凭借其注意力机制和并行计算能力，使人类情感分析任务变得直观。这些优势使得此类模型在语言任务上表现强大，而不同于循环神经网络因顺序处理耗时较长，在处理长文本时容易失效。本文旨在研究前沿的基于Transformer的语言模型在观点挖掘中的行为特性，并对其提供高层次比较，以突出各自的关键差异。此外，我们的比较研究为生产工程师指明了重点关注的路径方向，同时为研究人员提供了未来研究课题的指导方针。