Sentiment Analysis on Suicidal Tendency Affected by the COVID-19 Pandemic: A Comparison of Different Algorithms Using Twitter Data

Abstract

Background: Unintentional behavioral changes brought on by the COVID-19 outbreak may have contributed to the increase in reported suicidal attempts. The coronavirus pandemic era has contributed to modifying existing domestic violence, mental health, conflict, and anxiety. Moreover, quarantine and self-isolation may have resulted in melancholy, suicidal thoughts, drug and alcohol misuse, and loneliness. Therefore, it is crucial and significant to gather data on the global prevalence of suicide and suicidal attempts throughout the pandemic. Objective: This study's objective was to evaluate the tone of tweets regarding suicide and whether or not those tweets are connected to COVID-19. Methods: Twitter is one of the most widely used channels for sharing people's thoughts in various situations. A total of 9750 tweets have been found with respect to COVID-19-related suicidal ideation and other suicides. Gathered data were pre-processed, and feature vectors were constructed in order to establish a forecast paradigm by using artificial neural networks (ANN), long short-term memory (LSTM), and support vector machine (SVM). Results: The results demonstrated that ANN outperformed SVM and LSTM in terms of classification, achieving 91.33% accuracy while also having greater recall, precision, F-measure, and minimum error values. Conclusion: The findings of this study may help to categorize peoples' suicidal thoughts successfully. The results will help to identify future suicidal incidents with the help of the proposed approach and avoid such kinds of situations from occurring.