Depression Detection of User in Media Social Twitter Using Random Forest

Abstract

One of the disorders of mental health that often occurs in individuals is depression. Identifying depression in the first place is important for the individual. But in fact, conducting an early examination of depression still has some drawbacks. If it continues to be ignored, this can have an impact on the health of the individual. Therefore, there is a need for other methods that can represent the level of depression in individuals, through other media such as social media such as Twitter. Twitter has become one of the media to tell what users of the application experience or feel. This is encouraging to detect of depression in Twitter users. The data used is data taken from the results of the distribution of forms based on DASS-42 with a total of 159 Twitter users for each username taken 100 tweets. This study uses the Word2Vec extraction feature, to convert data from text to vector by looking at the relationship of each word and Random Forest as a classification method, to maintain the balance of data in different classes, especially very large data sets. Based on the test results, the Random Forest model produces an accuracy of 68.75%.