Detection of Suicidal Tweets Based On Naïve Bayes Algorithm

Abstract

The pandemic has created a trend where most people have become addicted to the social media to express thoughts, opinions, share reviews and also share their daily life online. This has contributed to the increasing volume of data over the social media platforms. Based on these situations, the social media data have been analyzed for the benefits of businesses and organizations. Various sentiment analysis research has been conducted to obtain public opinions such as on products and also on daily situations. Twitter has become one of the most visited social media due to the platform’s convenience and easiness in texting messages. This research has proposed to analyze the Twitter data for the detection of suicidal intention. This is to help many depressed people who have been affected by the Covid-19 pandemic by analyzing their opinions and thoughts online. Identifying this suicidal behaviour in tweets can be the first step in the suicide prevention. The objective of the research is to explore the Naïve Bayes algorithm’s capability to detect the suicide ideation tweets. The Twitter data were scrapped during the Malaysia’s pandemic lockdown in May 2021 using the Tweepy library. There were 5439 Twitter data that have been scrapped based on “stress”, “anxiety”, “depression” and “suicide” keywords. The evaluation results have shown that the algorithm has produced good and acceptable performance in detecting the suicidal tweets with 80.39% accuracy. The result also has shown that more people are actually depressed duirng the pandemic, especilly during the lockdown. Future works would be to compare the performance of Naïve Bayes with other well-known classifiers and also to consider scrapping and processing non-English words from other social media platforms such as the Facebook and Instagram.