@inproceedings{lopez-ubeda-etal-2019-detecting,
title = "Detecting Anorexia in {S}panish Tweets",
author = "L{\'o}pez {\'U}beda, Pilar and
Plaza del Arco, Flor Miriam and
D{\'\i}az Galiano, Manuel Carlos and
Urena Lopez, L. Alfonso and
Martin, Maite",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
month = sep,
year = "2019",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/R19-1077",
doi = "10.26615/978-954-452-056-4_077",
pages = "655--663",
abstract = "Mental health is one of the main concerns of today{'}s society. Early detection of symptoms can greatly help people with mental disorders. People are using social networks more and more to express emotions, sentiments and mental states. Thus, the treatment of this information using NLP technologies can be applied to the automatic detection of mental problems such as eating disorders. However, the first step to solving the problem should be to provide a corpus in order to evaluate our systems. In this paper, we specifically focus on detecting anorexia messages on Twitter. Firstly, we have generated a new corpus of tweets extracted from different accounts including anorexia and non-anorexia messages in Spanish. The corpus is called SAD: Spanish Anorexia Detection corpus. In order to validate the effectiveness of the SAD corpus, we also propose several machine learning approaches for automatically detecting anorexia symptoms in the corpus. The good results obtained show that the application of textual classification methods is a promising option for developing this kind of system demonstrating that these tools could be used by professionals to help in the early detection of mental problems.",
}
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<abstract>Mental health is one of the main concerns of today’s society. Early detection of symptoms can greatly help people with mental disorders. People are using social networks more and more to express emotions, sentiments and mental states. Thus, the treatment of this information using NLP technologies can be applied to the automatic detection of mental problems such as eating disorders. However, the first step to solving the problem should be to provide a corpus in order to evaluate our systems. In this paper, we specifically focus on detecting anorexia messages on Twitter. Firstly, we have generated a new corpus of tweets extracted from different accounts including anorexia and non-anorexia messages in Spanish. The corpus is called SAD: Spanish Anorexia Detection corpus. In order to validate the effectiveness of the SAD corpus, we also propose several machine learning approaches for automatically detecting anorexia symptoms in the corpus. The good results obtained show that the application of textual classification methods is a promising option for developing this kind of system demonstrating that these tools could be used by professionals to help in the early detection of mental problems.</abstract>
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%0 Conference Proceedings
%T Detecting Anorexia in Spanish Tweets
%A López Úbeda, Pilar
%A Plaza del Arco, Flor Miriam
%A Díaz Galiano, Manuel Carlos
%A Urena Lopez, L. Alfonso
%A Martin, Maite
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F lopez-ubeda-etal-2019-detecting
%X Mental health is one of the main concerns of today’s society. Early detection of symptoms can greatly help people with mental disorders. People are using social networks more and more to express emotions, sentiments and mental states. Thus, the treatment of this information using NLP technologies can be applied to the automatic detection of mental problems such as eating disorders. However, the first step to solving the problem should be to provide a corpus in order to evaluate our systems. In this paper, we specifically focus on detecting anorexia messages on Twitter. Firstly, we have generated a new corpus of tweets extracted from different accounts including anorexia and non-anorexia messages in Spanish. The corpus is called SAD: Spanish Anorexia Detection corpus. In order to validate the effectiveness of the SAD corpus, we also propose several machine learning approaches for automatically detecting anorexia symptoms in the corpus. The good results obtained show that the application of textual classification methods is a promising option for developing this kind of system demonstrating that these tools could be used by professionals to help in the early detection of mental problems.
%R 10.26615/978-954-452-056-4_077
%U https://aclanthology.org/R19-1077
%U https://doi.org/10.26615/978-954-452-056-4_077
%P 655-663
Markdown (Informal)
[Detecting Anorexia in Spanish Tweets](https://aclanthology.org/R19-1077) (López Úbeda et al., RANLP 2019)
ACL
- Pilar López Úbeda, Flor Miriam Plaza del Arco, Manuel Carlos Díaz Galiano, L. Alfonso Urena Lopez, and Maite Martin. 2019. Detecting Anorexia in Spanish Tweets. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 655–663, Varna, Bulgaria. INCOMA Ltd..