构建协作式AI框架，提升多模态情感分析效率与部署灵活性。
Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies
- 通过分阶段任务分解，整合ChatGPT、Gemini等生成式AI模型协同处理情感分析。
- 在边缘与云端联合部署，实现跨平台社交媒体情感分析的高效运行。
- 适合需要快速落地、多源数据融合的情感分析项目团队参考使用。
基于大语言模型（LLM）的人工智能技术进步彻底改变了情感分析领域，推动其从研究场景向工业级应用转型。然而，处理复杂多模态数据时需集成多种AI模型，且特征提取成本高昂，带来显著挑战。针对面向营销的软件开发需求，本文提出一种协作式AI框架，通过在不同AI系统间高效分配与协同任务来应对上述问题。首先，阐述了开发过程中提炼的关键解决方案，强调生成式AI模型如ChatGPT、Google Gemini在将复杂情感分析任务拆解为可管理的阶段性目标中的作用。此外，通过一个详尽案例研究，展示了该框架在边缘与云环境下的实际应用效果，验证其在多元在线媒体渠道中进行情感分析的有效性。
原文摘要 · Abstract (English)
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.
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