Abstract
Depression is a mental disorder with strong social and economic implications. Due to its relevance, recently several researches have explored the analysis of social media content to identify and track depressed users. Most approaches follow a supervised learning strategy supported on the availability of labeled training data. Unfortunately, acquiring such data is very complex and costly. To handle this problem, in this paper we propose a crosslingual approach based on the idea that data already labeled in a specific language can be leveraged to classify depression in other languages. The proposed method is based on a word-level alignment process. Particularly, we propose two representations for the alignment; one of them takes advantage of the psycholinguistic resource LIWC and the other uses bilingual word embeddings. For evaluating the proposed approach, we faced the detection of depression by employing English and Spanish tweets as the source and target data respectively. The results outperformed solutions based on automatic translation of texts, confirming the usefulness of the proposed approach.
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Notes
- 1.
In the experiments, the translation of the seed words was done by means of Google Translator.
- 2.
We are interested in using content features, therefore the stop words are ignored.
- 3.
- 4.
- 5.
An offensive term in Spanish.
References
Abdalla, M., Hirst, G.: Cross-lingual sentiment analysis without (good) translation. arXiv preprint arXiv:1707.01626 (2017)
Al-Shabi, A., Adel, A., Omar, N., Al-Moslmi, T.: Cross-lingual sentiment classification from english to arabic using machine translation. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 8(12), 434–440 (2017)
Álvarez-Carmona, M.Á.: Author profiling in social media with multimodal information. Ph.D. thesis, Instituto Nacional de Astrofísica, Óptica y Electrónica (2019)
Artetxe, M., Labaka, G., Agirre, E.: Learning bilingual word embeddings with (almost) no bilingual data. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 451–462 (2017)
Coppersmith, G., Dredze, M., Harman, C.: Quantifying mental health signals in Twitter. In: Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pp. 51–60 (2014)
Coppersmith, G., Dredze, M., Harman, C., Hollingshead, K., Mitchell, M.: Clpsych 2015 shared task: depression and PTSD on Twitter. In: Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pp. 31–39 (2015)
De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. In: Seventh International AAAI Conference on Weblogs and Social Media (2013)
Gliozzo, A., Strapparava, C.: Exploiting comparable corpora and bilingual dictionaries for cross-language text categorization. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, pp. 553–560. Association for Computational Linguistics (2006)
Guntuku, S.C., Yaden, D.B., Kern, M.L., Ungar, L.H., Eichstaedt, J.C.: Detecting depression and mental illness on social media: an integrative review. Curr. Opin. Beha. Sci. 18, 43–49 (2017)
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
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
Mikolov, T., Le, Q.V., Sutskever, I.: Exploiting similarities among languages for machine translation. arXiv preprint arXiv:1309.4168 (2013)
Nadeem, M.: Identifying depression on Twitter. arXiv preprint arXiv:1607.07384 (2016)
Pedersen, T.: Screening Twitter users for depression and PTSD with lexical decision lists. In: Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pp. 46–53 (2015)
Pennebaker, J.W., Booth, R.J., Francis, M.E.: LIWC2007: linguistic inquiry and word count. LIWC.net, Austin (2007)
Prettenhofer, P., Stein, B.: Cross-language text classification using structural correspondence learning. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 1118–1127 (2010)
Reece, A.G., Reagan, A.J., Lix, K.L., Dodds, P.S., Danforth, C.M., Langer, E.J.: Forecasting the onset and course of mental illness with twitter data. Sci. Rep. 7(1), 13006 (2017)
Ruder, S.: A survey of cross-lingual word embedding models. CoRR abs/1706.04902 (2017)
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. (CSUR) 34(1), 1–47 (2002)
Shen, G., et al.: Depression detection via harvesting social media: a multimodal dictionary learning solution. In: IJCAI, pp. 3838–3844 (2017)
Stankevich, M., Isakov, V., Devyatkin, D., Smirnov, I.: Feature engineering for depression detection in social media. In: ICPRAM, pp. 426–431 (2018)
Tsugawa, S., Kikuchi, Y., Kishino, F., Nakajima, K., Itoh, Y., Ohsaki, H.: Recognizing depression from Twitter activity. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 3187–3196 (2015)
Wei, B., Pal, C.: Cross lingual adaptation: an experiment on sentiment classifications. In: Proceedings of the ACL 2010 Conference Short Papers, pp. 258–262. Association for Computational Linguistics (2010)
Wolohan, J., Hiraga, M., Mukherjee, A., Sayyed, Z.A., Millard, M.: Detecting linguistic traces of depression in topic-restricted text: attending to self-stigmatized depression with NLP. In: Proceedings of the First International Workshop on Language Cognition and Computational Models, pp. 11–21 (2018)
Yang, X., McCreadie, R., Macdonald, C., Ounis, I.: Transfer learning for multi-language Twitter election classification. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 341–348 (2017)
Acknowledgments
This work was partially supported by CONACYT under scholarship 869498, postdoctoral fellowship CVU-174410 and project grant CB-2015-01-257383.
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Coello-Guilarte, L., Ortega-Mendoza, R.M., Villaseñor-Pineda, L., Montes-y-Gómez, M. (2019). Crosslingual Depression Detection in Twitter Using Bilingual Word Alignments. In: Crestani, F., 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_2
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