Dynamic emotion modelling and anomaly detection in conversation based on emotional transition tensor
作者: Xiao SunChen ZhangLian Li
作者单位: 1School of Computer and Information, Hefei University of Technology, Hefei 230009, China
刊名: Information Fusion, 2019, Vol.46 , pp.11-22
来源数据库: Elsevier Journal
DOI: 10.1016/j.inffus.2018.04.001
关键词: Hybrid deep learning modelEmotional transitionAnomaly detectionSocial conversation
原始语种摘要: Abstract(#br)Conversational data in social media contain a great deal of useful information, and conversation anomaly detection is an important research direction in the field of sentiment analysis. Each user has his or her own specific emotional characteristic, and by studying the distribution and sampling the users’ emotional transitions, we can simulate specific emotional transitions in the conversations. Anomaly detection in conversation data refers to detecting users’ abnormal opinions and sentiment patterns as well as special temporal aspects of such patterns. This paper proposes a hybrid model that combines the convolutional neural network long short-term memory (CNN-LSTM) with a Markov chain Monte Carlo (MCMC) method to identify users’ emotions, sample users’ emotional transition...
全文获取路径: Elsevier  (合作)
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影响因子:2.262 (2012)

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关键词翻译
关键词翻译
  • transition 转移
  • emotional 情绪的
  • conversation 通话
  • tensor 张量
  • detection 探测
  • temporal 现世的
  • anomaly 异常
  • model 模型
  • detecting 检测
  • simulate 模拟