EMPLOYING LONG SHORT-TERM MEMORY NETWORKS IN TRIGGER DETECTION FOR EMETOPHOBIA
DOI:
https://doi.org/10.24193/subbi.2020.2.02Keywords:
machine learning, text classification, long short-term memory, trigger detection, natural language processing, neural networks.Abstract
Research focused on mental health-related issues is vital to the modern person’s life. Specific phobias are part of the anxiety disorder umbrella and they are distressing afflictions. Emetophobia is the rarely known, yet fairly common and highly disruptive specific phobia of vomiting. Unlike other phobias, emetophobia is triggered not only by the object of the specific fear, but also by verbal and written mentions of said object. This paper proposes and compares ten neural network-based architectures that discern between triggering and non-triggering groups of written words. An interface is created, where the best models can be used in emetophobia related applications. This interface is then integrated into an application that can be used by emetophobes to censor online content such that the exposure to triggers is controlled, patient-centered, and patient-paced.
References
Badjatiya, P., Gupta, S., Gupta, M., and Varma, V. Deep learning for hate speech detection in tweets. In Proceedings of ACM WWW’17 Companion (Republic and Canton of Geneva, CHE, 2017), WWW ’17 Companion, International World Wide Web Conferences Steering Committee, p. 759–760.
Bird, S., Klein, E., and Loper, E. Natural language processing with Python: analyzing text with the natural language toolkit. ”O’Reilly Media, Inc.”, 2009.
Davidson, T., Warmsley, D., Macy, M., and Weber, I. Automated hate speech detection and the problem of offensive language. ICWSM (2017), 512–515.
Djuric, N., Zhou, J., Morris, R., Grbovic, M., Radosavljevic, V., and Bhamidipati, N. Hate speech detection with comment embeddings. In Proceedings of the 24th International Conference on World Wide Web (New York, NY, USA, 2015), WWW ’15 Companion, Association for Computing Machinery, p. 29–30.
Do, H. T.-T., Huynh, H. D., Van Nguyen, K., Nguyen, N. L.-T., and Nguyen, A. G.-T. Hate speech detection on vietnamese social media text using the bidirectional lstm model. In Proceedings of VLSP 2019 (2019).
Fellbaum, C. WordNet: An Electronic Lexical Database. Bradford Books, 1998. [7] Mircea, M.-M. Emetophobia dataset, Aug. 2020.
Pennington, J., Socher, R., and Manning, C. D. Glove: Global vectors for word representation. In In EMNLP (2014).
Ritchie, H., and Roser, M. Mental health. Our World in Data (2018). https://ourworldindata.org/mental-health.
van Hout, W. J. P. J., and Bouman, T. K. Clinical features, prevalence and psychiatric complaints in subjects with fear of vomiting. Clinical Psychology & Psychotherapy 19, 6 (2012), 531–539.
Warner, W., and Hirschberg, J. Detecting hate speech on the world wide web. In Proceedings of the Second Workshop on Language in Social Media (Montr´eal, Canada, June 2012), Association for Computational Linguistics, pp. 19–26.
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