北京师范大学学报（自然科学版）2025，Vol.61Issue(6)：751-757,7.DOI:10.12202/j.0476-0301.2025142
大语言模型驱动的期货市场新闻多主题和多层次情感分析框架
Large language model-driven multi-topic and multi-level sentiment analysis framework for futures market news
摘要
Abstract
To address the need for systematic and in-depth mining of complex sentiment signals from futures market news,in this paper a large language model-driven multi-topic and multi-level sentiment analysis framework is proposed.A"macro-topic-specific aspect/event"hierarchical strategy is applied to this framework to construct a topic system that covers multi-dimensional market elements,to achieve accurate discrimination of topic-level sentiments.Aspect-sentiment-opinion triplet extraction and event sentiment analysis techniques are incorporated to identify sentiment impact of key market elements and unexpected events.Low-rank adaptation is employed to achieve efficient domain adaptation of a localized large language model,to validate applicability of large language models in financial text analysis.The proposed framework is found to perform rather well across multiple sentiment analysis tasks,effectively distinguishing the sentiment tendencies of different topics and deeply mining complex sentiment signals within news texts.This work provides a systematic solution for in-depth sentiment analysis of futures market news and offers reliable technical support for the quantitative analysis of the impact of news sentiment on the futures market.关键词
人工智能/金融市场/大语言模型/期货市场/情感分析Key words
artificial intelligence/financial market/large language model/futures market/sentiment analysis分类
信息技术与安全科学引用本文复制引用
王鹤,陆亦王,刘夏璞,黄海量..大语言模型驱动的期货市场新闻多主题和多层次情感分析框架[J].北京师范大学学报（自然科学版）,2025,61(6):751-757,7.基金项目
国家自然科学基金资助项目(72442024) （72442024）
