用英语和阿拉伯语推文的情绪分析来检测Twitter用户的抑郁症
AbdelMoniem Helmy1, Radwa Nassar1, Nagy Ramdan1
1Department of Information Systems and Technology Faculty of Graduate Studies for Statistical Research, Cairo University, Egypt.
Artificial intelligence in medicine
|January 6, 2024
概括
这项研究开发了用于检测阿拉伯语和英语社交媒体文本中抑郁症的机器学习模型. 这些模型的准确性很高,可以通过在线平台提前识别心理健康问题.
科学领域:
- 计算语言学计算语言学
- 心理健康信息学心理健康信息学
- 自然语言处理自然语言处理.
背景情况:
- 抑郁症带来重大风险,包括自杀念头和日常残疾.
- 早期发现抑郁症对于及时干预和降低死亡率至关重要.
- 社交媒体平台为识别抑郁症早期症状提供了丰富的资源.
研究的目的:
- 开发和评估机器学习模型以检测阿拉伯语和英语社交媒体文本中的抑郁症.
- 为培训和验证抑郁症检测模型创建注释体.
- 为实时抑郁症检测和趋势预测构建一个Web应用程序.
主要方法:
- 使用Twitter数据开发五种用于抑郁症检测的机器学习模型.
- 创建一个手动注释的阿拉伯语语库 (Arabic_Dep_tweets_10,000).
- 生成两个自动注释的英语语体 (Eng_without_negation_60.000和Eng_with_negation_57.000).
主要成果:
- 最好的阿拉伯模型在二进制分类中获得了96.6%的f1分数.
- 对于没有否定的英语文本,模型达到92% (二进制) 和88% (多类) 的f1-score.
- 对于带有否定的英语文本,模型获得了87% (二进制) 和85% (多类) 的f1-分数.
结论:
- 机器学习模型可以有效地从阿拉伯语和英语的社交媒体文本中检测抑郁症.
- 开发的身体和模型有助于大规模的抑郁症查和分析.
- 有一个Web应用程序可用于实际实施使用社交媒体数据检测抑郁症.
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