Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies

TL;DR

This paper presents a collaborative AI framework leveraging large language models to improve sentiment analysis by distributing tasks across multiple AI systems, reducing complexity and costs in processing multimodal data.

Contribution

It introduces a novel collaborative AI architecture that integrates generative models like ChatGPT and Google Gemini for efficient sentiment analysis across edge and cloud environments.

Findings

Effective task distribution across AI systems reduces processing complexity.

Generative AI models simplify complex sentiment analysis tasks.

Case study demonstrates improved sentiment analysis across diverse media channels.

Abstract

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.