Depression Detection of Users in Social-Media Twitter Using Decision Tree with Word2Vec
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Abstract
Social media is a medium or place on the Internet that allows users to be themselves. Interact, cooperate, share, and communicate with other users virtually. Not only do users share happy feelings, but they also share their emotions and sentiments towards a particular issue. Which sometimes makes users look depressed when they deliver it. Depression itself is the most commonly encountered mental illness, which makes the sufferer feel sad, lonely, inferior, and disconnected from the people around them. And even worse, depression can make the sufferer have suicidal thoughts. Therefore, we need to know whether the user indicated being depressed or not to prevent unwanted things by using a depression measurement tool scale called DASS 42 for data labeling. To detect depression, we can use the sufferer's Twitter account to take data based on tweets from the user and change the entire dataset to a vector using both the architectures of Word 2, Vec Skip-Gram, and CBOW. In this research, we utilize a decision tree to detect depression. The best results were obtained from the Word2Vec Skip-Gram model with a data ratio of 90:10 using the Gini criterion parameter and a maximum depth value of 20, resulting in an accuracy of 93% and a f1-score of 94%.
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