从规则到大模型，系统梳理情感分析的演进路径。
Comprehensive Study on Sentiment Analysis: From Rule-based to modern LLM based system
- 梳理从词典规则到深度学习的演变脉络
- 指出双语处理、反讽识别等核心挑战
- 适合想了解情感分析全貌的研究者
本文全面综述了人工智能与大语言模型背景下情感分析的发展。情感分析作为自然语言处理的关键任务，已从传统基于规则的方法演变为先进的深度学习技术。本研究回顾了情感分析的历史演进，重点分析从词典基础与模式匹配方法向更复杂的机器学习和深度学习模型的转变过程。讨论了处理双语文本、识别反讽以及缓解偏见等关键挑战。综述了当前前沿方法，识别了新兴趋势，并提出了未来研究方向。通过整合现有方法并探索未来机遇，旨在深入理解情感分析在人工智能与大模型背景下的发展现状与前景。
原文摘要 · Abstract (English)
This paper provides a comprehensive survey of sentiment analysis within the context of artificial intelligence (AI) and large language models (LLMs). Sentiment analysis, a critical aspect of natural language processing (NLP), has evolved significantly from traditional rule-based methods to advanced deep learning techniques. This study examines the historical development of sentiment analysis, highlighting the transition from lexicon-based and pattern-based approaches to more sophisticated machine learning and deep learning models. Key challenges are discussed, including handling bilingual texts, detecting sarcasm, and addressing biases. The paper reviews state-of-the-art approaches, identifies emerging trends, and outlines future research directions to advance the field. By synthesizing current methodologies and exploring future opportunities, this survey aims to understand sentiment analysis in the AI and LLM context thoroughly.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。