Predicting Emotional Reaction in Social Networks

  • Jérémie ClosEmail author
  • Anil BandhakaviEmail author
  • Nirmalie Wiratunga
  • Guillaume Cabanac
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10193)


Online content has shifted from static and document-oriented to dynamic and discussion-oriented, leading users to spend an increasing amount of time navigating online discussions in order to participate in their social network. Recent work on emotional contagion in social networks has shown that information is not neutral and affects its receiver. In this work, we present an approach to detect the emotional impact of news, using a dataset extracted from the Facebook pages of a major news provider. The results of our approach significantly outperform our selected baselines.


Root Mean Square Error Rating Vector Multilinear Regression Word Embedding Emotion Lexicon 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Binali, H., Wu, C., Potdar, V.: Computational approaches for emotion detection in text. In: 4th IEEE International Conference on Digital Ecosystems and Technologies, pp. 172–177. IEEE (2010)Google Scholar
  2. 2.
    Kramer, A.D., Guillory, J.E., Hancock, J.T.: Experimental evidence of massive-scale emotional contagion through social networks. Proc. Natl. Acad. Sci. 111(24), 8788–8790 (2014)CrossRefGoogle Scholar
  3. 3.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in NIPS, pp. 3111–3119 (2013)Google Scholar
  4. 4.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  5. 5.
    Qiyao, W., Zhengmin, L., Yuehui, J., Shiduan, C., Tan, Y.: ULM: a user-level model for emotion prediction in social networks. China Univ. Posts Telecommun. 23, 63–88 (2016)CrossRefGoogle Scholar
  6. 6.
    Rao, Y., Lei, J., Wenyin, L., Li, Q., Chen, M.: Building emotional dictionary for sentiment analysis of online news. World Wide Web 17(4), 723–742 (2014)CrossRefGoogle Scholar
  7. 7.
    Ribeiro, F.N., Araújo, M., Gonçalves, P., Gonçalves, M.A., Benevenuto, F.: Sentibench-a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Sci. 5(1), 1–29 (2016)CrossRefGoogle Scholar
  8. 8.
    Staiano, J., Guerini, M.: DepecheMood: a lexicon for emotion analysis from crowd-annotated news. In: Proceedings of the 52nd Annual Meeting of the ACL, pp. 427–433 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Robert Gordon UniversityAberdeenUK
  2. 2.Université de ToulouseToulouseFrance

Personalised recommendations