Abstract
In this work we introduce Catenae, a new library whose main goal is to provide an easy-to-use solution for scalable real-time deployments with Python micromodules. To demonstrate its potential, we have developed an application that processes social media data and alerts about early signs of depression. The architecture has the following modules: (1) a crawler for extracting users and content, (2) a classifier pipeline that processes new user contents, (3) an HTTP API for alert management and access to users’ submissions, and (4) a web interface.
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References
Losada, D.E., Crestani, F.: A test collection for research on depression and language use. In: Fuhr, N., Quaresma, P., Gonçalves, T., Larsen, B., Balog, K., Macdonald, C., Cappellato, L., Ferro, N. (eds.) CLEF 2016. LNCS, vol. 9822, pp. 28–39. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44564-9_3
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., Lawless, S., Gonzalo, J., Kelly, L., Goeuriot, L., Mandl, T., Cappellato, L., Ferro, N. (eds.) CLEF 2017. LNCS, vol. 10456, pp. 346–360. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65813-1_30
Acknowledgements
This work has been supported by MINECO (TIN2014-54565-JIN, TIN2015-64282-R), Xunta de Galicia (ED431G/08) and European Regional Development Fund.
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Martínez-Castaño, R., Pichel, J.C., Losada, D.E., Crestani, F. (2018). A Micromodule Approach for Building Real-Time Systems with Python-Based Models: Application to Early Risk Detection of Depression on Social Media. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_79
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DOI: https://doi.org/10.1007/978-3-319-76941-7_79
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