SIDetect : A Web-Base Application for Detecting Suicidal Ideations (SI) Using Natural Language Processing and Machine Learning /
Cañas, Neil Michael D.
SIDetect : A Web-Base Application for Detecting Suicidal Ideations (SI) Using Natural Language Processing and Machine Learning / Neil Michael D. Cañas, Carlo M. Salva, Saeymond Charles N. Serrano, Jihad M. Yusoph. - x, 124 pages : illustrations ; 29 cm. + 1 CD-ROM (4 3/4 in.)
Thesis (Undergraduate)
College of Science --
Includes bibliographical references.
This thesis introduces SIDetect, a web-based application that uses machine learning (ML) and natural language processing (NLP) methods to identify suicidal ideations (SI). The system uses natural language processing (NLP) to extract pertinent information from user-generated text, which is then examined using machine learning (ML) algorithms to find patterns that suggest the presence of suicidal ideations. A dataset of user-generated text with SI and non-SI instances was used to assess the system. It was developed using Visual Studio Code, Open API, and Python as the programming language. The user interface of the system was also assessed and found to be intuitive and user-friendly. The system was evaluated using ISO 25010 criteria for quality assurance, with a total of 23 respondents consisting of IT/CS, Psychology students, and professionals from the IT and Psychology field. According to the evaluation's findings, SIDetect achieved an overall score of 3.55, indicating a Highly Acceptable rating.--Author's Abstract
Suicidal behavior--Computer programs.
BTH QA 76 / C36 2023
SIDetect : A Web-Base Application for Detecting Suicidal Ideations (SI) Using Natural Language Processing and Machine Learning / Neil Michael D. Cañas, Carlo M. Salva, Saeymond Charles N. Serrano, Jihad M. Yusoph. - x, 124 pages : illustrations ; 29 cm. + 1 CD-ROM (4 3/4 in.)
Thesis (Undergraduate)
College of Science --
Includes bibliographical references.
This thesis introduces SIDetect, a web-based application that uses machine learning (ML) and natural language processing (NLP) methods to identify suicidal ideations (SI). The system uses natural language processing (NLP) to extract pertinent information from user-generated text, which is then examined using machine learning (ML) algorithms to find patterns that suggest the presence of suicidal ideations. A dataset of user-generated text with SI and non-SI instances was used to assess the system. It was developed using Visual Studio Code, Open API, and Python as the programming language. The user interface of the system was also assessed and found to be intuitive and user-friendly. The system was evaluated using ISO 25010 criteria for quality assurance, with a total of 23 respondents consisting of IT/CS, Psychology students, and professionals from the IT and Psychology field. According to the evaluation's findings, SIDetect achieved an overall score of 3.55, indicating a Highly Acceptable rating.--Author's Abstract
Suicidal behavior--Computer programs.
BTH QA 76 / C36 2023