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.

翻译：本文对人工智能（AI）和大语言模型（LLM）背景下的情感分析进行了全面综述。情感分析作为自然语言处理（NLP）的关键领域，已从传统的基于规则方法显著演进至先进的深度学习技术。本研究审视了情感分析的历史发展，重点阐述了从基于词典和基于模式的方法向更复杂的机器学习及深度学习模型的转变。文中讨论了关键挑战，包括处理双语文本、检测讽刺以及应对偏见。本文回顾了最先进的方法，识别了新兴趋势，并概述了推动该领域发展的未来研究方向。通过综合现有方法论并探索未来机遇，本综述旨在深入理解AI与LLM背景下的情感分析。