Machine Learning Based Approach For Prediction Of Suicide Related Activity

• Published in: 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC) pp. 967 - 972
• Main Authors: Patel, Hemal, Soni, Neha
• Format: Conference Proceeding
• Language: English
• Published: IEEE 07.10.2021
• Subjects: Classification Methods; Dictionaries; Linguistics; Machine learning; Predictive models; Radio frequency; Sensitivity; Social networking (online); Suicide; Support vector machines; Twitter
• DOI: 10.1109/ICOSEC51865.2021.9591836

Suicidal ideation is a significant public health issue that kills a large number of people every year all around the world. Every year, over 800,000 people commit suicide all over the world. As their use of social networking sites has grown, users have utilized them to discuss personal problems, including exchanging suicide plans. Suicide can be prevented by analyzing such posts available on social media sites, either manually or automatically. Suicide-related subjects are gathered from social media sites, which leads to a spike in suicide ideation. Prior research has found individual terms or phrases from tweets that predict suicide risk factors, but this study will use the n-gram model to compute scores, which combines Unigram, Bigram, and Trigram with a dictionary. Suicide attempts will be categorized into three groups.: low, medium, and high risk. The purpose of this research is to use a range of important data, such as linguistic and location-based factors, as well as emoticons, to better identify cases of suicide. Machine learning-based approaches such as Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), as well as Extra Tree, will be used to categories suicidal cases.