Aspect-based Sentiment Analysis (ABSA) is a critical task in Natural Language Processing (NLP) that focuses on extracting sentiments related to specific aspects within a text, offering deep insights into customer opinions. Traditional sentiment analysis methods, while useful for determining overall sentiment, often miss the implicit opinions about particular product or service features. This paper presents a comprehensive review of the evolution of ABSA methodologies, from lexicon-based approaches to machine learning and deep learning techniques. We emphasize the recent advancements in Transformer-based models, particularly Bidirectional Encoder Representations from Transformers (BERT) and its variants, which have set new benchmarks in ABSA tasks. We focused on finetuning Llama and Mistral models, building hybrid models using the SetFit framework, and developing our own model by exploiting the strengths of state-of-the-art (SOTA) Transformer-based models for aspect term extraction (ATE) and aspect sentiment classification (ASC). Our hybrid model Instruct - DeBERTa uses SOTA InstructABSA for aspect extraction and DeBERTa-V3-baseabsa-V1 for aspect sentiment classification. We utilize datasets from different domains to evaluate our model's performance. Our experiments indicate that the proposed hybrid model significantly improves the accuracy and reliability of sentiment analysis across all experimented domains. As per our findings, our hybrid model Instruct - DeBERTa is the best-performing model for the joint task of ATE and ASC for both SemEval restaurant 2014 and SemEval laptop 2014 datasets separately. By addressing the limitations of existing methodologies, our approach provides a robust solution for understanding detailed consumer feedback, thus offering valuable insights for businesses aiming to enhance customer satisfaction and product development.

翻译：方面级情感分析（ABSA）是自然语言处理（NLP）中的一项关键任务，旨在提取文本中与特定方面相关的情感，从而深入洞察客户意见。传统的情感分析方法虽可用于判断整体情感倾向，却常忽略针对特定产品或服务特征的隐含观点。本文全面回顾了ABSA方法的发展历程，涵盖从基于词典的方法到机器学习及深度学习技术。我们重点探讨了基于Transformer模型的最新进展，特别是双向编码器表示模型（BERT）及其变体，这些模型已在ABSA任务中树立了新的性能基准。本研究专注于对Llama和Mistral模型进行微调，利用SetFit框架构建混合模型，并通过整合前沿（SOTA）基于Transformer的模型在方面术语提取（ATE）与方面情感分类（ASC）任务中的优势开发了自主模型。我们的混合模型Instruct-DeBERTa采用SOTA模型InstructABSA进行方面提取，并采用DeBERTa-V3-baseabsa-V1进行方面情感分类。我们使用多领域数据集评估模型性能，实验结果表明所提出的混合模型在所有实验领域中均显著提升了情感分析的准确性与可靠性。根据研究结果，对于SemEval 2014餐厅数据集和SemEval 2014笔记本电脑数据集的ATE与ASC联合任务，我们的混合模型Instruct-DeBERTa在各自数据集上均表现出最优性能。通过克服现有方法的局限性，本方法为理解细粒度消费者反馈提供了稳健解决方案，从而为旨在提升客户满意度与产品开发的企业提供了宝贵洞见。