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

  • Xiao Sun
  • Chen Zhang
  • Shuai Ding
  • Changqin Quan
Article
  • 239 Downloads

Abstract

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.

Keywords

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

Notes

Acknowledgments

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.

References

  1. 1.
    Ahmad S, Lavin A, Purdy S, Agha Z (2017) Unsupervised real-time anomaly detection for streaming data. NeurocomputingGoogle Scholar
  2. 2.
    Chen K, Lei J (2014) Network cross-validation for determining the number of communities in network data. Br J Psychiatry 178(5):410CrossRefGoogle Scholar
  3. 3.
    Clauset A, Shalizi CR, Newman MEJ (2009) Power-law distributions in empirical data. Siam Rev 51(4):661–703MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12 (1):2493–2537MATHGoogle Scholar
  5. 5.
    Dang X, Serfling R (2010) Nonparametric depth-based multivariate outlier identifiers, and masking robustness properties. J Stat Plann Infer 140(1):198–213MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Diehl PU, Neil D, Binas J, Cook M (2015) Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing. In: International Joint Conference on Neural Networks, pp 1–8Google Scholar
  7. 7.
    Eghbali HJ (1979) K-s test for detecting changes from landsat imagery data. IEEE Trans Syst Man Cybern 9(1):17–23CrossRefGoogle Scholar
  8. 8.
    Guzman J, Poblete B (2013) On-line relevant anomaly detection in the twitter stream:an efficient bursty keyword detection model. In: ACM SIGKDD Workshop on Outlier Detection and Description, pp 31–39Google Scholar
  9. 9.
    He Y, Deng W, Zhang D, School B, University S (2014) Study on sentiments recognition and classification of chinese micro-blog. J Intell 33(3):136–139Google Scholar
  10. 10.
    Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780CrossRefGoogle Scholar
  11. 11.
    Hu X, Tang J, Zhang Y, Liu H (2013) Social spammer detection in microblogging. In: International Joint Conference on Artificial Intelligence, pp 2633–2639Google Scholar
  12. 12.
    Huang G, Song S, Gupta JN, Wu C (2014) Semi-supervised and unsupervised extreme learning machines. IEEE Trans Cybern 44(12):2405–2417CrossRefGoogle Scholar
  13. 13.
    Id T, Lozano AC, Abe N, Liu Y (2009) Proximity-based anomaly detection using sparse structure learning. In: Siam International Conference on Data Mining, SDM 2009, Sparks, pp 97–108Google Scholar
  14. 14.
    Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. Eprint Arxiv 1Google Scholar
  15. 15.
    Karpathy A, Johnson J, Li FF (2015) Visualizing and understanding recurrent networksGoogle Scholar
  16. 16.
    Kim Y (2014) Convolutional neural networks for sentence classification. Eprint ArxivGoogle Scholar
  17. 17.
    Le QV, Mikolov T (2014) Distributed representations of sentences and documents. Comput Sci 4:1188–1196Google Scholar
  18. 18.
    Liang J, Du R (2007) Model-based fault detection and diagnosis of hvac systems using support vector machine method. Int J Refrig 30(6):1104–1114CrossRefGoogle Scholar
  19. 19.
    Lin H, Jia J, Qiu J, Zhang Y, Shen G, Xie L, Tang J, Feng L, Chua TS Detecting stress based on social interactions in social networks. IEEE Trans Knowl Data Eng PP(99): 1–1Google Scholar
  20. 20.
    Ling-Yun LI, Ji AO, Qiao Z, Jian LI (2015) Research on security event real-time monitoring framework based on micro-blog. Netinfo SecurityGoogle Scholar
  21. 21.
    Luming Z, Mingli S, Zicheng L, Xiao L, Jiajun B, Chun C (2013) Newblock probabilistic graphlet cut: Exploring spatial structure cue for weakly supervised image segmentation. In: Proceedings of CVPR2013Google Scholar
  22. 22.
    Luming Z, Yahong H, Yi Y, Mingli S, Shuicheng Y, Qi T (2013) Discovering discriminative graphlets for aerial image categories recognition. IEEE T-IP 22(12):5071–5084MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Luming Z, Yi Y, Yue G, Changbo W, Yi Y, Xuelong L (2014) A probabilistic associative model for segmenting weakly-supervised images. IEEE T-IP 23(9):4150–4159MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Luming Z, Yue G, Roger Z, Qi T, Xuelong L (2014) Fusion of multi-channel local and global structural cues for photo aesthetics evaluation. IEEE T-IP 23(3):1419–1429. ISSN:1057-7149CrossRefMATHGoogle Scholar
  25. 25.
    Luming Z, Yue G, Rongrong J, Qionghai D, Xuelong L (2014) Actively learning human gaze shifting paths for semantics-aware photo cropping. IEEE T-IP 23 (5):2235–2245. ISSN:1057-7149MathSciNetCrossRefMATHGoogle Scholar
  26. 26.
    Ma SH, Wang J, Liu Z, Jiang HY (2012) Density-based distributed elliptical anomaly detection in wireless sensor networks. Appl Mech Mater 249-250:226–230CrossRefGoogle Scholar
  27. 27.
    Micro-blog user development report in 2016 (2017) http://www.useit.com.cn/thread-14392-1-1.html
  28. 28.
    Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. Proc Icml:807–814Google Scholar
  29. 29.
    Nguyen DT, Jung JE (2017) Real-time event detection for online behavioral analysis of big social data. Fut Gener Comput Syst 66:137–145CrossRefGoogle Scholar
  30. 30.
    Ren H, Ye Z, Li Z (2017) Anomaly detection based on a dynamic markov model. Information SciencesGoogle Scholar
  31. 31.
    Rosen DM, Kaess M, Leonard JJ (2014) Rise: An incremental trust-region method for robust online sparse least-squares estimation. IEEE Trans Robot 30 (5):1091–1108CrossRefGoogle Scholar
  32. 32.
    Ruder S, Ghaffari P, Breslin JG (2016) Deep learning for multilingual aspect-based sentiment analysis. In: Proceedings of SemEval-2016, pp 330–336Google Scholar
  33. 33.
    Salamon J, Bello J (2016) Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Process Lett PP (99):1–1Google Scholar
  34. 34.
    Sun X, Li C, Ren F (2016) Sentiment analysis for chinese microblog based on deep neural networks with convolutional extension features. Neurocomputing 210:227–236CrossRefGoogle Scholar
  35. 35.
    Tang D, Qin B, Liu T (2016) Aspect level sentiment classification with deep memory network. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, p 214C224Google Scholar
  36. 36.
    Wang Z, Joo V, Tong C, Xin X, Chin HC (2014) Anomaly detection through enhanced sentiment analysis on social media data. In: IEEE International Conference on Cloud Computing Technology and Science, pp 917–922Google Scholar
  37. 37.
    Wang J, Yu LC, Lai KR, Zhang X (2016) Dimensional sentiment analysis using a regional cnn-lstm model. In: Meeting of the Association for Computational LinguisticsGoogle Scholar
  38. 38.
    Yang F, Liu Y, Yu X, Yang M (2012) Automatic detection of rumor on sina weibo, pp 1–7Google Scholar
  39. 39.
    Yasami Y, Safaei F (2016) A statistical infinite feature cascade-based approach to anomaly detection for dynamic social networks. Comput Commun 100:52–64CrossRefGoogle Scholar
  40. 40.
    Yin G, Zhang Y, Dong Y, Yuan W, Dong H (2013) A boost factor based detection method for abnormal rank of microblogging. J Harbin Eng Univ 34 (4):488–493Google Scholar
  41. 41.
    Yu H, Yang J, Han J, Li X (2005) Making svms scalable to large data sets using hierarchical cluster indexing. Data Min Knowl Discov 11(3):295–321MathSciNetCrossRefGoogle Scholar
  42. 42.
    Yuan SF, Wang ST (2013) Multi-classification method applied to face recognition based on mixed gaussian distribution. Appl Res Comput 30(9):2868–2871Google Scholar
  43. 43.
    Zhang X, Lecun Y (2015) Text understanding from scratch. Computer ScienceGoogle Scholar
  44. 44.
    Zhang Y, Marshall I, Wallace BC (2016) Rationale-augmented convolutional neural networks for text classificationGoogle Scholar
  45. 45.
    Zhou ZH, Zhang HR, Xie J (2014) Data crawler for sina weibo based on python. J Comput Appl 34(11):3131–3134Google Scholar
  46. 46.
    Zhou C, Sun C, Liu Z, Lau FCM (2015) A c-lstm neural network for text classification. Computer ScienceGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Xiao Sun
    • 1
  • 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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