Sentiment Analysis from Tweets for Depression Level Prediction
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Abstract
Currently, Thai people are increasingly suffering from depression, and these patients often do not know that they are depressed and often express themselves through social media because it is a form of communication through channels that do not rely on facial expressions. Therefore, this research presents sentiment analysis from Twitter users' tweets to predict their level of depression. Tweets used in the study include text, emoticons, and images. Sentiment analysis of those tweets applies hybrid machine learning, a combination of recursive feature selection using support vector machine and random forest modeling. The experimental results indicated that the developed provided the highest efficiency. The most important feature for predicting depression levels was the tweet's text type.
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References
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