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

• URL: http://arxiv.org/abs/2410.13247v2
• Date: Wed, 23 Oct 2024 11:09:57 GMT
• Title: Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies
• Authors: Chaofeng Zhang, Jia Hou, Xueting Tan, Gaolei Li, Caijuan Chen,
• Abstract summary: Large language model (LLM) based artificial intelligence technologies have been a game-changer, particularly in sentiment analysis. However, integrating diverse AI models for processing complex multimodal data and the associated high costs of feature extraction presents significant challenges. This study introduces a collaborative AI framework designed to efficiently distribute and resolve tasks across various AI systems.
• Score: 3.3374611485861116
• License:
• 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.

Related papers

• Generative AI Application for Building Industry [10.154329382433213]
  This paper investigates the transformative potential of generative AI technologies, particularly large language models (LLMs) in the building industry. The research highlights how LLMs can automate labor-intensive processes, significantly improving efficiency, accuracy, and safety in building practices.
  arXiv  Detail & Related papers  (2024-10-01T21:59:08Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence [0.0]
  The proposed physics-aware generative AI platform, AtomAgents, synergizes the intelligence of large language models (LLM) Our results enable accurate prediction of key characteristics across alloys and highlight the crucial role of solid solution alloying to steer the development of advanced metallic alloys.
  arXiv  Detail & Related papers  (2024-07-13T22:46:02Z)
• Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology using Large Language Models -- A Case in Optimizing Intermodal Freight Transportation [1.6230958216521798]
  This study investigates the potential of leveraging the pre-trained Large Language Models (LLMs) By adopting ChatGPT API as the reasoning core, we outline an integrated workflow that encompasses natural language processing, methontology-based prompt tuning, and transformers. The outcomes of our methodology are knowledge graphs in widely adopted ontology languages (e.g., OWL, RDF, SPARQL)
  arXiv  Detail & Related papers  (2024-05-29T16:40:31Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• An Interactive Agent Foundation Model [49.77861810045509]
  We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents. Our training paradigm unifies diverse pre-training strategies, including visual masked auto-encoders, language modeling, and next-action prediction. We demonstrate the performance of our framework across three separate domains -- Robotics, Gaming AI, and Healthcare.
  arXiv  Detail & Related papers  (2024-02-08T18:58:02Z)
• A Vision for Operationalising Diversity and Inclusion in AI [5.4897262701261225]
  This study seeks to envision the operationalization of the ethical imperatives of diversity and inclusion (D&I) within AI ecosystems. A significant challenge in AI development is the effective operationalization of D&I principles. This paper proposes a vision of a framework for developing a tool utilizing persona-based simulation by Generative AI (GenAI)
  arXiv  Detail & Related papers  (2023-12-11T02:44:39Z)
• Bridging MDE and AI: A Systematic Review of Domain-Specific Languages and Model-Driven Practices in AI Software Systems Engineering [1.4853133497896698]
  This study aims to investigate the existing model-driven approaches relying on DSL in support of the engineering of AI software systems. The use of MDE for AI is still in its early stages, and there is no single tool or method that is widely used.
  arXiv  Detail & Related papers  (2023-07-10T14:38:38Z)
• AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges [60.56413461109281]
  Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
  arXiv  Detail & Related papers  (2023-04-10T15:38:12Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.