Using NLP and Binary Classification for Predicting Suicidal Tendencies Based on Textual Input

dc.contributor.author	Cruz, Ronell Mathew R.
dc.date.accessioned	2024-05-14T00:58:26Z
dc.date.available	2024-05-14T00:58:26Z
dc.date.issued	2023-06
dc.identifier.uri	http://dspace.cas.upm.edu.ph:8080/xmlui/handle/123456789/2686
dc.description.abstract	Suicide is death caused by injuring oneself with the intent to die. Suicidal tendency is a certain set of behavior that an individual exhibits when being suicidal, which includes posting cryptic messages in social media. This is a study that uses the Suicide and Depression Dataset and applies NLP and preprocessing techniques to transform the data. Machine learning models are then used to predict the class of the entries. Logistic Regression is the best performing model with an accuracy score of 93.24%. The best performing model is integrated and used in a Discord bot application.	en_US
dc.subject	Machine Learning	en_US
dc.subject	Natural Language Processing	en_US
dc.subject	Hashing Vectorizer	en_US
dc.subject	Tokenizer	en_US
dc.subject	Stemming	en_US
dc.title	Using NLP and Binary Classification for Predicting Suicidal Tendencies Based on Textual Input	en_US
dc.type	Thesis	en_US