In recent years,social media platforms have played a critical role in mitigation for a wide range of disasters.The highly up-to-date social responses and vast spatial coverage from millions of citizen sensors enable a...In recent years,social media platforms have played a critical role in mitigation for a wide range of disasters.The highly up-to-date social responses and vast spatial coverage from millions of citizen sensors enable a timely and comprehensive disaster investigation.However,automatic retrieval of on-topic social media posts,especially considering both of their visual and textual information,remains a challenge.This paper presents an automatic approach to labeling on-topic social media posts using visual-textual fused features.Two convolutional neural networks(CNNs),Inception-V3 CNN and word embedded CNN,are applied to extract visual and textual features respectively from social media posts.Well-trained on our training sets,the extracted visual and textual features are further concatenated to form a fused feature to feed the final classification process.The results suggest that both CNNs perform remarkably well in learning visual and textual features.The fused feature proves that additional visual feature leads to more robustness compared with the situation where only textual feature is used.The on-topic posts,classified by their texts and pictures automatically,represent timely disaster documentation during an event.Coupling with rich spatial contexts when geotagged,social media could greatly aid in a variety of disaster mitigation approaches.展开更多
群体研讨支持系统(Group Argument Support Systems,GASS)的匿名、并行输入及自动化记录群体发言的特征,在辅助群体产生大量有价值观点的同时,也常常导致"信息过载"和"知识断层"。介绍了一个自动化聚类工具来增强...群体研讨支持系统(Group Argument Support Systems,GASS)的匿名、并行输入及自动化记录群体发言的特征,在辅助群体产生大量有价值观点的同时,也常常导致"信息过载"和"知识断层"。介绍了一个自动化聚类工具来增强群体的认知能力并提高电子会议的效率。首先识别了GASS环境下自动化主题聚类的一些挑战并回顾了相关研究,结合GASS的研讨模式、研讨文本特征及中文文本分析的要求,给出了中文分词、停词表处理以及有效词语识别的文本分析技术。提出基于主题分析的特征向量选择方法,并基于自组织映射的神经网络思想,用Java语言设计并开发了一个自动聚类工具。实验表明,该工具可以达到0.28的聚类准确率,0.35的聚类全面率,产生0.83的聚类错误率。展开更多
基金University of South Carolina [grant number 13540-18-48955].
文摘In recent years,social media platforms have played a critical role in mitigation for a wide range of disasters.The highly up-to-date social responses and vast spatial coverage from millions of citizen sensors enable a timely and comprehensive disaster investigation.However,automatic retrieval of on-topic social media posts,especially considering both of their visual and textual information,remains a challenge.This paper presents an automatic approach to labeling on-topic social media posts using visual-textual fused features.Two convolutional neural networks(CNNs),Inception-V3 CNN and word embedded CNN,are applied to extract visual and textual features respectively from social media posts.Well-trained on our training sets,the extracted visual and textual features are further concatenated to form a fused feature to feed the final classification process.The results suggest that both CNNs perform remarkably well in learning visual and textual features.The fused feature proves that additional visual feature leads to more robustness compared with the situation where only textual feature is used.The on-topic posts,classified by their texts and pictures automatically,represent timely disaster documentation during an event.Coupling with rich spatial contexts when geotagged,social media could greatly aid in a variety of disaster mitigation approaches.
文摘群体研讨支持系统(Group Argument Support Systems,GASS)的匿名、并行输入及自动化记录群体发言的特征,在辅助群体产生大量有价值观点的同时,也常常导致"信息过载"和"知识断层"。介绍了一个自动化聚类工具来增强群体的认知能力并提高电子会议的效率。首先识别了GASS环境下自动化主题聚类的一些挑战并回顾了相关研究,结合GASS的研讨模式、研讨文本特征及中文文本分析的要求,给出了中文分词、停词表处理以及有效词语识别的文本分析技术。提出基于主题分析的特征向量选择方法,并基于自组织映射的神经网络思想,用Java语言设计并开发了一个自动聚类工具。实验表明,该工具可以达到0.28的聚类准确率,0.35的聚类全面率,产生0.83的聚类错误率。
