家庭暴力的分类 基于主题建模和集体学习的社交媒体中的波斯文本内容
Meysam Salehi1, Shahrbanoo Ghahari1
1Department of Mental Health, School of Behavioral Sciences and Mental Health (Tehran institute of psychiatry), Iran University of Medical Sciences, Tehran, Iran.
Heliyon
|November 25, 2024
概括
这项研究开发了一种高性能的波斯家庭暴力文本分类模型,使用主题建模和集体学习. 该模型有助于社交媒体监控,并为预防性干预提供了洞察力.
科学领域:
- 社会科学 社会科学 社会科学
- 计算机科学 计算机科学
- 公共卫生 公共卫生
背景情况:
- 社交媒体平台产生了关于家庭暴力的大量波斯语内容,特别是在COVID-19后.
- 监测这些内容对于了解趋势和告知预防策略至关重要.
研究的目的:
- 为家庭暴力内容开发高性能波斯文本分类模型.
- 利用主题建模和集体学习来改进分类和知识提取.
主要方法:
- 收集了来自Telegram,Twitter和Instagram的波斯家庭暴力数据 (2020年4月至2023年4月).
- 应用潜伏迪里克莱特分配 (LDA) 用于主题建模.
- 使用堆叠和投票组合方法进行分类.
主要成果:
- 确定了五个主要主题:家庭犯罪新闻,战争暴力,妇女权利和暴力反应.
- 堆叠组合模型以96.45%的精度,准确性,回忆率和F-score实现了卓越的性能.
结论:
- 开发的模型为社交媒体上的波斯语家庭暴力文本提供了高效的多类分类.
- 提取的主题见解可以支持心理健康中心开发有针对性的预防干预措施.
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