Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies
This work addresses practical deployment needs for marketing-oriented software development, though it appears incremental in applying existing models to a collaborative setup.
The study tackled the challenge of integrating diverse AI models for complex multimodal sentiment analysis by introducing a collaborative AI framework, which was demonstrated to be effective in a case study analyzing sentiments across online media channels.
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.