Ensembled Machine Learning Methods and Feature Extraction Approaches for Suicide-Related Social Media - Merinda Lestandy; Abdurrahim Abdurrahim; Amrul Faruq; M. Irfan; Novendra Setyawan (Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha, 2024)
Suicide is a pressing public health concern that affects both young people and adults. The widespread use of mobile devices and social networking has facilitated the gathering of data, allowing academics to assess patterns, concepts, emotions, and opinions expressed on these platforms. This study is to detect suicidal inclinations using Reddit online dataset. It allows for the identification of people who express thoughts of suicide by analyzing their postings. The method addresses and evaluates different machine learning classification models, namely linear SVC, random forest, and ensemble learning, along with feature extraction approaches such as TF-IDF, Bag of Words, and VADER. This study utilised a voting classifier in our ensemble model, where the projected class output is selected by the class with the highest probability. This approach, typically known as a