用大模型实现跨领域细粒度情感分析，准确率达92%。
Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models
- 基于大模型构建跨域情感分析框架，自动提取产品特征与情感。
- 在SemEval-2015数据集上达到92%的准确率。
- 适合做产品评论分析、客户反馈挖掘的企业用户。
基于方面的情感分析（ABSA）是一种精细化的情感分析方法，旨在根据产品、服务或实体的具体方面（如质量、价格、服务）提取并分类情感。与传统情感分析仅对全文打分不同，ABSA将文本拆解为具体方面，分别评估其情感倾向，从而更精细地理解用户意见，帮助企业在产品改进和客户满意度提升方面做出数据驱动决策。该过程包括方面抽取、情感分类及方面级情感聚合。本研究探索了大语言模型在完整跨领域方面情感分析中的能力，提出可复用于类似场景的框架。实验表明，在SemEval-2015任务12的ABSA数据集上，模型可达到92%的准确率。
原文摘要 · Abstract (English)
Aspect-based sentiment analysis (ASBA) is a refined approach to sentiment analysis that aims to extract and classify sentiments based on specific aspects or features of a product, service, or entity. Unlike traditional sentiment analysis, which assigns a general sentiment score to entire reviews or texts, ABSA focuses on breaking down the text into individual components or aspects (e.g., quality, price, service) and evaluating the sentiment towards each. This allows for a more granular level of understanding of customer opinions, enabling businesses to pinpoint specific areas of strength and improvement. The process involves several key steps, including aspect extraction, sentiment classification, and aspect-level sentiment aggregation for a review paragraph or any other form that the users have provided. ABSA has significant applications in areas such as product reviews, social media monitoring, customer feedback analysis, and market research. By leveraging techniques from natural language processing (NLP) and machine learning, ABSA facilitates the extraction of valuable insights, enabling companies to make data-driven decisions that enhance customer satisfaction and optimize offerings. As ABSA evolves, it holds the potential to greatly improve personalized customer experiences by providing a deeper understanding of sentiment across various product aspects. In this work, we have analyzed the strength of LLMs for a complete cross-domain aspect-based sentiment analysis with the aim of defining the framework for certain products and using it for other similar situations. We argue that it is possible to that at an effectiveness of 92\% accuracy for the Aspect Based Sentiment Analysis dataset of SemEval-2015 Task 12.
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