结合台风属性数据和多标签分类方法,以BERT-BiLSTM(Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory)为分类模型,提出基于微博文本与深度学习的台风灾情识别方法,对2010—2019年登陆广...结合台风属性数据和多标签分类方法,以BERT-BiLSTM(Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory)为分类模型,提出基于微博文本与深度学习的台风灾情识别方法,对2010—2019年登陆广东省的强台风/超强台风灾情进行识别,在粗分类获取台风灾情相关微博文本的基础上,进一步细分类为交通影响、社会影响、电力影响、林业影响和内涝积水等5类灾情。结果表明:1)提出的台风灾情识别方法粗分类和细分类精度分别达到0.907和0.814;2)强台风/超强台风的灾情占比受台风强度、路径和受灾地区发展水平等因素影响而存在差异;3)台风登陆前,灾情主要为台风预防措施导致的交通影响和社会影响。台风登陆后,灾情表现出单峰和双峰特征,反映台风灾情的变化趋势和特点。展开更多
Suicide has become a critical concern,necessitating the development of effective preventative strategies.Social media platforms offer a valuable resource for identifying signs of suicidal ideation.Despite progress in ...Suicide has become a critical concern,necessitating the development of effective preventative strategies.Social media platforms offer a valuable resource for identifying signs of suicidal ideation.Despite progress in detecting suicidal ideation on social media,accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge.To tackle this,we have developed a dataset focused on Chinese suicide narratives from Weibo’s Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships(TCNN-SN).This model enhances predictive performance by leveraging social network relationship features and applying correction factors within a weighted linear fusion framework.It is specifically designed to identify key individuals who can help uncover hidden suicidal users and clusters.Our model,assessed using the bespoke dataset and benchmarked against alternative classification approaches,demonstrates superior accuracy,F1-score and AUC metrics,achieving 88.57%,88.75%and 94.25%,respectively,outperforming traditional TextCNN models by 12.18%,10.84%and 10.85%.We assert that our methodology offers a significant advancement in the predictive identification of individuals at risk,thereby contributing to the prevention and reduction of suicide incidences.展开更多
文摘结合台风属性数据和多标签分类方法,以BERT-BiLSTM(Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory)为分类模型,提出基于微博文本与深度学习的台风灾情识别方法,对2010—2019年登陆广东省的强台风/超强台风灾情进行识别,在粗分类获取台风灾情相关微博文本的基础上,进一步细分类为交通影响、社会影响、电力影响、林业影响和内涝积水等5类灾情。结果表明:1)提出的台风灾情识别方法粗分类和细分类精度分别达到0.907和0.814;2)强台风/超强台风的灾情占比受台风强度、路径和受灾地区发展水平等因素影响而存在差异;3)台风登陆前,灾情主要为台风预防措施导致的交通影响和社会影响。台风登陆后,灾情表现出单峰和双峰特征,反映台风灾情的变化趋势和特点。
基金funded by Outstanding Youth Team Project of Central Universities(QNTD202308).
文摘Suicide has become a critical concern,necessitating the development of effective preventative strategies.Social media platforms offer a valuable resource for identifying signs of suicidal ideation.Despite progress in detecting suicidal ideation on social media,accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge.To tackle this,we have developed a dataset focused on Chinese suicide narratives from Weibo’s Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships(TCNN-SN).This model enhances predictive performance by leveraging social network relationship features and applying correction factors within a weighted linear fusion framework.It is specifically designed to identify key individuals who can help uncover hidden suicidal users and clusters.Our model,assessed using the bespoke dataset and benchmarked against alternative classification approaches,demonstrates superior accuracy,F1-score and AUC metrics,achieving 88.57%,88.75%and 94.25%,respectively,outperforming traditional TextCNN models by 12.18%,10.84%and 10.85%.We assert that our methodology offers a significant advancement in the predictive identification of individuals at risk,thereby contributing to the prevention and reduction of suicide incidences.
