Detecting anomalous emotion through big data from social networks based on a deep learning method

  • Xiao SunEmail author
  • Chen Zhang
  • Shuai Ding
  • Changqin Quan


Anomaly detection in social media refers to the detection of users’ abnormal opinions, sentiment patterns, or special temporal aspects of such patterns. Social media platforms, such as Sina Weibo or Twitter, provide a Big-data platform for information retrieval, which include user feedbacks, opinions, and information on most issues. This paper proposes a hybrid neural network model called Convolutional Neural Network-Long-Short Term Memory(CNN-LSTM), we successfully applies the model to sentiment analysis on a microblog Big-data platform and obtains significant improvements that enhance the generalization ability. Based on the sentiment of a single post in Weibo, this study also adopted the multivariate Gaussian model and the power law distribution to analyze the users’ emotion and detect abnormal emotion on microblog, the multivariate Gaussian method automatically captures the correlation between different features of the emotions and saves a certain amount of time through the batch calculation of the joint probability density of data sets. Through the measure of a joint probability density value and validation of the corpus from social network, anomaly detection accuracy of an individual user is 83.49% and that for a different month is 87.84%. The results of the distribution test show that individual user’s neutral, happy, and sad emotions obey the normal distribution but the surprised and angry emotions do not. In addition, the group-based emotions on microblogs obey the power law distribution but individual emotions do not.


Big data Social media Hybrid deep learning model Multivariate Gaussian application Anomaly detection 



The work is supported by the Natural Science Foundation of Anhui Province (1508085QF119) and State Key Program of National Natural Science of China (61432004, 71571058, 61461045). This work was partially supported by the China Postdoctoral Science Foundation funded project (No.2015M580532 and No.2017T100447). This research has been partially supported by National Natural Science Foundation of China under Grant No.61472117.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Xiao Sun
    • 1
    Email author
  • Chen Zhang
    • 1
  • Shuai Ding
    • 2
  • Changqin Quan
    • 3
  1. 1.School of Computer and InformationHefei University of TechnologyHefeiChina
  2. 2.School of ManagementHefei University of TechnologyBaoHe DistrictChina
  3. 3.Department of Computational ScienceKobe UniversityNadaJapan

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