使用大型语言模型将社交媒体数据连接到特定领域的主题的平台:对学生心理健康的应用
Leonard Ruocco1,2, Yuqian Zhuang1,2, Raymond Ng1,3
1Data Science Institute, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada.
JAMIA open
|January 22, 2024
概括
本研究介绍了一个使用自然语言处理来分析社交媒体文本,识别研究主题的主题和相关数据的AI平台. 该工具帮助研究人员高效处理各种应用的非结构化数据.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 计算社会科学 计算社会科学
背景情况:
- 分析大量的非结构化文本数据,特别是来自社交媒体的文本数据,对研究人员来说是一个巨大的挑战.
- 在杂数据集中识别特定的主题和主题需要先进的分析工具.
研究的目的:
- 开发一种基于人工智能 (AI) 的软件平台,用于分析文本数据.
- 让用户能够识别连贯的主题和相关的数据段,与特定的研究主题 (TOI) 保持一致.
主要方法:
- 利用基于大型语言模型构建的最先进的无监督自然语言处理 (NLP) 方法.
- 用BERTopic对社交媒体帖子进行集群,并识别由词汇集合表示的不同主题.
- 开发了功能,将主题词与相应的句子联系起来,增强用户定义的TOI的相似性分析.
主要成果:
- 通过使用Reddit数据对大学生心理健康的两个案例研究来证明平台的实用性.
- 识别了与抑郁症相关的信号,以及它们与社交媒体数据中的新兴主题的联系.
- 成功地将非结构化数据解析成连贯的主题,并确定了与研究TOI相关的数据段.
结论:
- 该平台为研究人员提供了一种可访问和具有成本效益的工具,用于分析大型非结构化数据集.
- 由人工智能驱动的方法有助于提取连贯的主题和识别与特定研究兴趣相关的数据.
- 虽然最初专注于心理健康,但该平台的设计可以将其推广到各种研究领域.
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