A Comparative Analysis on Suicidal Ideation Detection Using NLP, Machine, and Deep Learning

Social networks are essential resources to obtain information about people’s opinions and feelings towards various issues as they share their views with friends family. Suicidal ideation detection via online social network analysis has emerged an research topic significant difficulties in the fields of NLP psychology recent years. With proper exploitation media, complicated early symptoms suicidal ideations can be discovered hence, it save many lives. This study offers a comparative multiple machine learning deep models identify thoughts from media platform Twitter. The principal purpose our is achieve better model performance than prior works recognize indications high accuracy avoid suicide attempts. We applied text pre-processing feature extraction approaches such CountVectorizer word embedding, trained several for goal. Experiments were conducted on dataset 49,178 instances retrieved live tweets by 18 non-suicidal keywords using Python Tweepy API. Our experimental findings reveal that RF highest classification score among algorithms, 93% F1 0.92. However, training classifiers embedding increases ML models, where BiLSTM reaches 93.6% 0.93 score.