Skip to main content

Language Modeling in Temporal Mood Variation Models for Early Risk Detection on the Internet

  • 771 Accesses

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11696)

Abstract

Early risk detection can be useful in different areas, particularly those related to health and safety. Two tasks are proposed at CLEF eRisk-2018 for predicting mental disorder using users posts on Reddit. Depression and anorexia disorders must be detected as early as possible. In this paper, we extend the participation of LIRMM (Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier) in both tasks. The proposed model addresses this problem by modeling the temporal mood variation detected from user posts. The proposed architectures use only textual information without any hand-crafted features or dictionaries. The basic architecture uses two learning phases through exploration of state-of-the-art text vectorizations and deep language models. The proposed models perform comparably to other contributions while further experiments shows that attentive based deep language models outperformed the shallow learning text vectorizations.

Keywords

  • Classification
  • Word2vec
  • Doc2vec
  • LSTM
  • Attention
  • Temporal variation
  • Depression
  • Anorexia

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-28577-7_21
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   69.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-28577-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   89.99
Price excludes VAT (USA)
Fig. 1.

Notes

  1. 1.

    Reddit is an open-source platform where community members (red-ditors) can submit content (posts, comments, or direct links), vote submissions, and the content entries are organized by areas of interests (subreddits).

References

  1. The national eating disorders association (NEDA): Envisioning a world without eating disorders. In: The newsletter of the National Eating Disorders Association. Issue 22 (2009)

    Google Scholar 

  2. World Health Organization: Depression and other common mental disorders: global health estimates. In: World Health Organization (2017). http://www.who.int/iris/handle/10665/254610

  3. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: International Conference on Learning Representations (ICLR), vol. abs/1409.0473, September 2014

    Google Scholar 

  4. Dozat, T., Manning, C.D.: Deep biaffine attention for neural dependency parsing. In: ICLR-2017 (2017)

    Google Scholar 

  5. Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 328–339 (2018)

    Google Scholar 

  6. Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. In: ICML. JMLR Workshop and Conference Proceedings, vol. 32, pp. 1188–1196. JMLR.org (2014)

    Google Scholar 

  7. Leite Barroso, M., Lucena Grangeiro Maranhão, T., Melo teixeira batista, H., Pereira de Brito Neves, F., Farias de Oliveira, G.: Social panic disorder and its impacts. Amadeus Int. Multidisciplinary J. 2, 1–17 (2018)

    Google Scholar 

  8. Losada, D.E., Crestani, F., Parapar, J.: eRISK 2017: CLEF lab on early risk prediction on the internet: experimental foundations. In: Jones, G.J.F., et al. (eds.) CLEF 2017. LNCS, vol. 10456, pp. 346–360. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65813-1_30

    CrossRef  Google Scholar 

  9. Losada, D.E., Crestani, F., Parapar, J.: Overview of eRisk: early risk prediction on the internet. In: Bellot, P., et al. (eds.) CLEF 2018. LNCS, vol. 11018, pp. 343–361. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98932-7_30

    CrossRef  Google Scholar 

  10. Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, HLT 2011, vol. 1, pp. 142–150 (2011)

    Google Scholar 

  11. Merity, S., Keskar, N.S., Socher, R.: Regularizing and optimizing LSTM language models. In: International Conference on Learning Representations (2018)

    Google Scholar 

  12. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, vol. 26, pp. 3111–3119. Curran Associates, Inc. (2013)

    Google Scholar 

  13. Mikolov, T., Yih, S.W.T., Zweig, G.: Linguistic regularities in continuous space word representations. In: (NAACL-HLT-2013) (2013)

    Google Scholar 

  14. Moulahi, B., Azé, J., Bringay, S.: DARE to care: a context-aware framework to track suicidal ideation on social media. In: Bouguettaya, A., et al. (eds.) WISE 2017. LNCS, vol. 10570, pp. 346–353. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68786-5_28

    CrossRef  Google Scholar 

  15. Paul, M.J., Dredze, M.: You are what you tweet: analyzing Twitter for public health. In: ICWSM (2011)

    Google Scholar 

  16. Ragheb, W., Moulahi, B., Azé, J., Bringay, S., Servajean, M.: Temporal mood variation: at the CLEF erisk-2018 tasks for early risk detection on the internet. In: Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum, 10–14 September 2018, Avignon, France (2018)

    Google Scholar 

  17. Taddy, M.: Document classification by inversion of distributed language representations. In: CoRR. vol. abs/1504.07295 (2015)

    Google Scholar 

  18. Trautmann, S., Rehm, J., Wittchen, H.: The economic costs of mental disorders: Do our societies react appropriately to the burden of mental disorders? In: EMBO (2016)

    Google Scholar 

  19. Trotzek, M., Koitka, S., Friedrich, C.: Linguistic metadata augmented classifiers at the CLEF 2017 task for early detection of depression. In: Working Notes of CLEF 2017 - Conference and Labs of the Evaluation Forum, vol. CEUR-WS 1866 (2017)

    Google Scholar 

  20. Trotzek, M., Koitka, S., Friedrich, C.: Utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences. IEEE Trans. Knowl. Data Eng. (2018)

    Google Scholar 

  21. Wang, H., Keskar, N.S., Xiong, C., Socher, R.: Identifying generalization properties in neural networks. In: ICLR (2019)

    Google Scholar 

Download references

Acknowledgments

We would like to acknowledge La Région Occitanie and l’Agglomération Béziers Méditerranée which finance the thesis of Waleed Ragheb as well as INSERM and CNRS for their financial support of CONTROV project.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Waleed Ragheb .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Ragheb, W., Azé, J., Bringay, S., Servajean, M. (2019). Language Modeling in Temporal Mood Variation Models for Early Risk Detection on the Internet. In: , et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2019. Lecture Notes in Computer Science(), vol 11696. Springer, Cham. https://doi.org/10.1007/978-3-030-28577-7_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-28577-7_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28576-0

  • Online ISBN: 978-3-030-28577-7

  • eBook Packages: Computer ScienceComputer Science (R0)