通过体验式学习增强人工智能:分析用户生成内容的含义

IF 13.3 1区 管理学 Q1 BUSINESS
Ashutosh Singh , Reeti Agarwal , Rsha Alghafes , Armando Papa
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引用次数: 0

摘要

人工智能(AI)已经进化为生成式人工智能,为用户提供了更大的好处。人工智能平台提供与人工智能相关的生成服务,支持用户的专业发展,并收集反馈,通过体验式学习来提升服务。然而,随着人工智能平台上的用户数量随着时间的推移而增长,以客户评论的形式理解大量非结构化数据集呈现出越来越严峻的挑战。我们采用先进的机器学习技术-主题建模和word2vec-从非结构化数据中提取更准确的见解。我们从2022年到2024年收集人工智能内容创作平台的客户评论。通过结合主题建模和word2vec,我们发现了有价值的见解。我们的分析确定了八个关键主题:游乐场、支持中心、内容实验室、生产力、用户体验、访问、业务助理和Remix。回归分析的主题显示,内容实验室、用户体验、商务助理和Remix在客户满意度得分方面更有利。负抽样的word2vec分析表明,与其他主题相比,Access和Playground具有更好的衔接得分。相反,诸如Content Lab、Productivity和Business Assistant等主题的内聚性得分较低,这表明这些主题中的单词之间的聚类较弱。我们的研究为人工智能平台管理者提供了一些有价值的见解,可以通过体验式学习进一步提升服务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Empowering AI with experiential learning: Implications from analysing user-generated content
Artificial intelligence (AI) has evolved into generative artificial intelligence, offering users even greater benefits. The AI platforms provide generative AI-related services to support users' professional development and gather feedback to enhance the service through experiential learning. However, comprehending large volumes of unstructured datasets in the form of customer reviews presents an increasingly serious challenge as the number of users on AI platforms grows over time. We employ advanced machine learning techniques-topic modelling and word2vec- to extract more accurate insights from unstructured data. We collect customer reviews from AI content-creation platforms from 2022 to 2024. By combining topic modelling and word2vec, we uncover valuable insights. Our analysis identifies eight key topics: Playground, Support Hub, Content Lab, Productivity, User Experience, Access, Business Assistant, and Remix. The topic of regression analysis reveals that Content Lab, User Experience, Business Assistant, and Remix are more favourable in terms of customer satisfaction scores. The word2vec analysis with negative sampling indicates that Access and Playground demonstrate better cohesion scores compared to other themes. Conversely, themes such as Content Lab, Productivity, and Business Assistant have lower cohesion scores, indicating weak clustering among words within these themes. Our research offers several valuable insights for AI platform managers, which can further enhance services through experiential learning.
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来源期刊
CiteScore
21.30
自引率
10.80%
发文量
813
期刊介绍: Technological Forecasting and Social Change is a prominent platform for individuals engaged in the methodology and application of technological forecasting and future studies as planning tools, exploring the interconnectedness of social, environmental, and technological factors. In addition to serving as a key forum for these discussions, we offer numerous benefits for authors, including complimentary PDFs, a generous copyright policy, exclusive discounts on Elsevier publications, and more.
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