Tweet Sentiment Analysis to Detect Suicide Intentions Through a Machine Learning Approach to Big Data Using the Naive Bayes Method
DOI:
https://doi.org/10.70609/gtech.v9i1.6154Keywords:
Sentiment Analysis, Machine Learning Approaches, Big Data, Mental Health, TwitterAbstract
Suicidal ideation is a serious mental health problem and is often difficult to detect in its early stages. Social media, especially Twitter, is one of the platforms widely used by individuals to express their feelings and emotional conditions, including expressions of suicidal ideation. This study aims to develop a machine learning model that can analyze the sentiment of tweets related to suicidal ideation using big data. The data used in this study consisted of tweets that had been processed for sentiment analysis, which were then classified into three sentiment categories, namely positive, negative, and neutral. The machine learning model applied was Naive Bayes. The results of the model evaluation showed that this model had an accuracy of 72%, with precision and recall values varying depending on the sentiment category. The highest precision was recorded in the negative and neutral categories (0.91), while the highest recall was recorded in the positive category (0.97). This study provides insight into the potential use of machine learning-based sentiment analysis to detect signs of suicidal ideation through big data from social media that can help in early detection of mental health problems.
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