The research paper being discussed proposes a novel framework for enhancing sentiment analysis using collaborative AI. The authors address challenges associated with integrating diverse AI models for processing complex multimodal data, particularly the high cost of feature extraction. Their collaborative AI system efficiently distributes tasks across different AI systems, leveraging the strengths of generative AI models like ChatGPT and Google Gemini. The paper presents a detailed architecture for this system, outlining its components, interactions, and operational mechanisms. They then validate the framework’s feasibility through a case study, analyzing public sentiment surrounding food delivery across various online media channels. The authors demonstrate the system’s capabilities for both cloud-based and local deployments, emphasizing its potential to automate sentiment analysis tasks and inform decision-making processes in businesses and organizations.
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