Instruct-DeBERTa：一种面向文本评论的方面级情感分析的混合方法
Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis on Textual Reviews
AI总结：
本文提出混合模型Instruct-DeBERTa，结合InstructABSA与DeBERTa-V3-baseabsa-V1分别进行方面提取和情感分类，在SemEval 2014数据集上显著提升了ABSA任务的准确性和可靠性。
摘要：
方面级情感分析（ABSA）是自然语言处理（NLP）中的一项关键任务，专注于提取文本中与特定方面相关的情感，从而为深入了解客户意见提供洞察。传统的情感分析方法虽然对确定整体情感很有用，但往往忽略了关于特定产品或服务特征的隐含意见。本文全面回顾了ABSA方法的演进，从基于词典的方法到机器学习和深度学习技术。我们强调了基于Transformer的模型的最新进展，特别是Transformer的双向编码器表示（BERT）及其变体，它们在ABSA任务中树立了新的基准。我们专注于微调Llama和Mistral模型，使用SetFit框架构建混合模型，并通过利用最先进的（SOTA）基于Transformer的模型在方面项提取（ATE）和方面情感分类（ASC）方面的优势来开发我们自己的模型。我们的混合模型Instruct-DeBERTa使用SOTA的InstructABSA进行方面提取，并使用DeBERTa-V3-baseabsa-V1进行方面情感分类。我们利用来自不同领域的数据集来评估模型的性能。实验表明，所提出的混合模型显著提高了所有实验领域的情感分析的准确性和可靠性。根据我们的发现，我们的混合模型Instruct-DeBERTa是分别在SemEval restaurant 2014和SemEval laptop 2014数据集上联合执行ATE和ASC任务表现最佳的模型。通过解决现有方法的局限性，我们的方法为理解详细的消费者反馈提供了稳健的解决方案，从而为旨在提高客户满意度和产品开发的企业提供了有价值的洞察。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
