Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913560722931712 |
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| author | Zhang, Chaofeng Hou, Jia Tan, Xueting Li, Gaolei Chen, Caijuan |
| author_facet | Zhang, Chaofeng Hou, Jia Tan, Xueting Li, Gaolei Chen, Caijuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13247 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies Zhang, Chaofeng Hou, Jia Tan, Xueting Li, Gaolei Chen, Caijuan Software Engineering Artificial Intelligence Human-Computer Interaction 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. |
| title | Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies |
| topic | Software Engineering Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2410.13247 |