Sentiment Analysis Using Different Machine Learning Techniques on Social Media for Detecting Suicidal Tendency

Abstract

Suicide is a major problem in latest civilization. Previously diagnosis and reduction of suicide tries are crucial for saving people's lives. Suicidal thoughts can be detected using clinical approaches involving social workers or specialists, as well as ML methods using DL to identify online social material. This study is the first to introduce and describe the approaches in these areas. In this regard, the issue of creating ways for detecting persons who are predisposed to suicide conduct is becoming increasingly relevant. One of the approaches of this research is to look for typological aspects of suicide-related speech utilizing mathematical linguistics, computerized text processing, and machine learning. In foreign science, texts written by persons who were motivated by suicide are researched using automatic text processing methods, machine learning approaches, and models designed to classify whether the content is connected to suicide or not. It should go without saying that analyzing suicide notes together with other writings written by individuals who have taken their own lives is essential to developing techniques for detecting individuals who are at risk of suicide. There are two datasets such as suicidal and sentimental with total 2520 data and total 16 attributes. Passive-Aggressive, Random Forest, logistic regression, SVM, MultinomialNB algorithms are using for sentiment dataset which find best PassiveAggressive (73%) accuracy and XGBClassifier (96%) accuracy for suicidal datasets.