Anxiety Detection using Text Mining in the Social Network Era: A Literature Review
Abstract
Mental disorders are increasingly common, mainly anxiety. This disorder, when not detected in time, can become something serious, leading to extremes such as suicide. However, since several people who suffer from it opt for online interaction, there is the possibility of resorting to text mining focused on social networks. In this sense, with the present work we sought to review the bibliography that reports studies employing text mining to determine the users who suffered from anxiety through their publications or comments on their social networks. The review was organized based on the phases of text mining; that is, data collection, preprocessing, and classification. Among the aspects to be highlighted are (i) the tendency to use a social network to obtain data, especially Twitter; (ii) the relevance of data cleaning, applying techniques such as lemmatization; (iii) the most prominent algorithms in anxiety detection, such as Naive Bayes, logistic regression, SVM, and random forest. Beyond the contributions of the reviewed articles, it can be noted that there is still a need to develop more models that detect the disorder of interest.
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