情感分析中的协同人工智能：系统架构、数据预测与部署策略
Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies
AI总结：
本文提出一个协同人工智能框架，通过生成式AI模型将复杂情感分析任务分解为分阶段目标，并在边缘与云端部署，以解决多模态数据处理和特征提取成本高的挑战。
摘要：
基于大语言模型（LLM）的人工智能技术的进步已经改变了游戏规则，尤其是在情感分析领域。这一进展使得该技术从高度专业化的研究环境转向行业内的广泛实际应用。然而，集成多样化的人工智能模型以处理复杂的多模态数据，以及特征提取所带来的高昂成本，构成了重大挑战。受面向营销的软件开发需求驱动，我们的研究引入了一个协同人工智能框架，旨在高效地在各种人工智能系统之间分配和解决任务，以应对这些问题。首先，我们阐明了从开发过程中得出的关键解决方案，强调了生成式人工智能模型（如ChatGPT、Google Gemini）在将复杂的情感分析任务简化为可管理的分阶段目标方面的作用。此外，我们展示了一个在边缘和云端使用我们的协同人工智能系统的详细案例研究，展示了该系统在分析跨多种在线媒体渠道的情感方面的有效性。
英文摘要：
The advancement of large language model (LLM) based artificial intelligence technologies has been a game-changer, particularly in sentiment analysis. This progress has enabled a shift from highly specialized research environments to practical, widespread applications within the industry. However, integrating diverse AI models for processing complex multimodal data and the associated high costs of feature extraction presents significant challenges. Motivated by the marketing oriented software development +needs, our study introduces a collaborative AI framework designed to efficiently distribute and resolve tasks across various AI systems to address these issues. Initially, we elucidate the key solutions derived from our development process, highlighting the role of generative AI models like \emph{chatgpt}, \emph{google gemini} in simplifying intricate sentiment analysis tasks into manageable, phased objectives. Furthermore, we present a detailed case study utilizing our collaborative AI system in edge and cloud, showcasing its effectiveness in analyzing sentiments across diverse online media channels.
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。
