使用文本挖掘技术和分类模型分析儿童发展事实和神话
Mehedi Tajrian1, Azizur Rahman1, Muhammad Ashad Kabir1
1School of Computing, Mathematics and Engineering, Charles Sturt University, NSW, Australia.
Heliyon
|September 12, 2024
概括
这项研究使用文本挖掘和机器学习来区分儿童发展神话和在线事实. 使用词袋的逻辑回归实现了90%的准确性,帮助家长找到可靠的儿童发展信息.
科学领域:
- 计算语言学 计算语言学
- 儿童发展研究 儿童发展研究
- 人工智能的人工智能
背景情况:
- 关于儿童发展的在线错误信息对儿童的福祉构成风险.
- 现有的研究还没有充分地解决使用计算方法的儿童发育神话和事实之间的区别.
研究的目的:
- 开发和评估文本挖掘和机器学习模型,以将儿童发展信息分类为神话或事实.
- 为父母和照顾者提供一个工具,以确定可靠的儿童发展资源.
主要方法:
- 从公开可用的儿童发展网站收集数据.
- 文本挖掘用于数据预处理的应用.
- 评估六个机器学习 (ML) 分类器和一个深度学习 (DL) 模型,使用两个特征提取技术 (字袋) 和交叉验证 (k-fold,leave-one-out).
主要成果:
- 使用字袋 (BoW) 的物流回归 (LR) 实现了最高的分类准确率,达到90%.
- LR表现出异常的速度和效率,测试时间为0.97微秒/语句.
- 该模型有效地区分了事实和神话儿童发展信息.
结论:
- 逻辑回归结合词袋是一种高度准确和高效的方法来分类儿童发展信息.
- 这种方法可以作为一种有价值的工具来打击错误信息,并支持父母的知情决策.
- 进一步的研究可以将这种方法扩展到健康和育儿信息的其他领域.
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