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International Symposium on String Processing and Information Retrieval

SPIRE 2018: String Processing and Information Retrieval pp 168–182Cite as

Early Commenting Features for Emotional Reactions Prediction

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11147)

Abstract

Nowadays, one of the main sources for people to access and read news are social media platforms. Different types of news trigger different emotional reactions to users who may feel happy or sad after reading a news article. In this paper, we focus on the problem of predicting emotional reactions that are triggered on users after they read a news post. In particular, we try to predict the number of emotional reactions that users express regarding a news post that is published on social media. In this paper, we propose features extracted from users’ comments published about a news post shortly after its publication to predict users’ the triggered emotional reactions. We explore two different sets of features extracted from users’ comments. The first group represents the activity of users in publishing comments whereas the second refers to the comments’ content. In addition, we combine the features extracted from the comments with textual features extracted from the news post. Our results show that features extracted from users’ comments are very important for the emotional reactions prediction of news posts and that combining textual and commenting features can effectively address the problem of emotional reactions prediction.

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Fig. 1.
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Notes

  1. 1.

    https://www.facebook.com/.

  2. 2.

    https://twitter.com/.

  3. 3.

    https://code.google.com/p/word2vec/.

  4. 4.

    https://www.facebook.com/nytimes/.

  5. 5.

    https://developers.facebook.com/.

  6. 6.

    Facebook allows users to select an emotional reaction with regards to a post.

  7. 7.

    We use Random Forest because it obtained the best results on the run trained on terms among the various classifiers that we tried including SVM and Logistic Regression.

  8. 8.

    http://scikit-learn.org/.

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Acknowledgments

This paper was partially funded by the Swiss National Science Foundation (SNSF) under the project OpiTrack.

The work of the second author was partially funded by the the Spanish MINECO under the research project SomEMBED (TIN2015-71147-C2-1-P).

This paper is partially supported by the BIGDATAGRAPES project (grant agreement N. 780751) that received funding from the European Union’s Horizon 2020 research and innovation programme under the Information and Communication Technologies programme.

Author information

Authors and Affiliations

  1. Faculty of Informatics, Università della Svizzera italiana, Lugano, Switzerland

    Anastasia Giachanou & Fabio Crestani

  2. PRHLT Research Center, Universitat Politècnica de València, Valencia, Spain

    Paolo Rosso

  3. ISTI-CNR, Pisa, Italy

    Ida Mele

Authors
  1. Anastasia Giachanou
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  2. Paolo Rosso
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  3. Ida Mele
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  4. Fabio Crestani
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Corresponding author

Correspondence to Anastasia Giachanou .

Editor information

Editors and Affiliations

  1. Diego Portales University, Santiago, Chile

    Travis Gagie

  2. The University of Melbourne, Melbourne, VIC, Australia

    Alistair Moffat

  3. University of Chile, Santiago, Chile

    Gonzalo Navarro

  4. Universidad de Ingeniería y Tecnología, Lima, Peru

    Ernesto Cuadros-Vargas

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Giachanou, A., Rosso, P., Mele, I., Crestani, F. (2018). Early Commenting Features for Emotional Reactions Prediction. In: Gagie, T., Moffat, A., Navarro, G., Cuadros-Vargas, E. (eds) String Processing and Information Retrieval. SPIRE 2018. Lecture Notes in Computer Science(), vol 11147. Springer, Cham. https://doi.org/10.1007/978-3-030-00479-8_14

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  • DOI: https://doi.org/10.1007/978-3-030-00479-8_14

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