Abstract
Taking one’s own life is a tragic reaction to stressful situations in life. There is a noticeable increase in the ratio of number of suicides every year in Telangana [1]. Most of them are adolescents and youngsters and others too. So there is an urging need of research to be done on suicidal ideation and preventive methods to support mental health professionals and psychotherapists. So this paper aims in developing technological solutions to the problem. Suicides can be prevented if we could identify the mental health conditions of a person with ideations and predict the severity in earlier [2]. So in this paper, we applied machine learning algorithms to categorize persons with suicidal ideations from the data that is maintained or recorded during visit of an adolescent with a mental health professional in textual form of questionnaires. The data is recorded in native Telugu language during the session, as most of cases are from illiterates [1, 3]. So in order to classify the patient test data with more accuracy, there is a need of language corpus in Telugu with ideations. So this paper would give a great insight into creation of suicidal language or ideation corpora in native language Telugu.
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Soumya, K., Garg, V.K. (2021). Building a Language Data Set in Telugu Using Machine Learning Techniques to Address Suicidal Ideation and Behaviors in Adolescents. In: Choudhary, A., Agrawal, A.P., Logeswaran, R., Unhelkar, B. (eds) Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering, vol 778. Springer, Singapore. https://doi.org/10.1007/978-981-16-3067-5_1
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