在微博上识别早期怀孕:机器学习和基于词典的方法
Samer Muthana Sarsam1, Ahmed Ibrahim Alzahrani2, Hosam Al-Samarraie3,4
1School of Strategy and Leadership, Coventry University, Coventry, United Kingdom.
Heliyon
|October 9, 2023
概括
这项研究引入了一种基于情绪的系统,使用Twitter数据来检测早期怀孕. 分析用户的情绪和情绪可以帮助在社交媒体平台上识别潜在的怀孕.
科学领域:
- 计算社会科学 计算社会科学
- 数字健康数字健康
- 自然语言处理自然语言处理.
背景情况:
- 怀孕伴随着重大的医疗和心理社会风险,需要积极的健康监测.
- 早期发现怀孕对于及时干预和改善母亲健康结果至关重要.
- 社交媒体平台提供了丰富的实时用户生成数据来源,以获得与健康相关的见解.
研究的目的:
- 建议和评估一种基于情绪的机制,用于使用Twitter数据检测早期怀孕.
- 分析社交媒体帖子中表达的与怀孕相关的情绪和情绪.
- 探索微博情绪对识别早期怀孕的有用性.
主要方法:
- 收集并处理实时的Twitter数据,以提取与怀孕相关的情绪 (愤怒,恐惧,悲伤,快乐,惊喜) 和情绪极性.
- 对于情感和情感分析,使用了NRC影响强度词典和SentiStrength.
- 部分语音标记和关联规则挖掘被用来绘制怀孕术语与情绪的地图.
主要成果:
- 与怀孕相关的推特表现出高度的积极性,有明显的喜悦,悲伤和恐惧的表达.
- 分析揭示了与早期怀孕讨论有关的明显的情绪和情感模式.
- 分类模型证明了使用社交媒体情绪用于早期怀孕识别的可行性.
结论:
- 对社交媒体数据的情感和情感分析可以促进早期怀孕检测.
- 这种方法为医疗保健决策者提供了关于公共卫生趋势的有价值的实时见解.
- 利用社交媒体数据为了解用户的健康状况和需求提供了一条新的途径.
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