Contrast pattern-based classification on sentiment features for detecting people with mental disorders on social media

Mental disorders are a global problem that widely affects different segments of the population. Mental disorders present consequences in the life of those suffering from them as they can have difficulties performing daily tasks normally. However, consequences in the economy, society, human rights, and cultural scope are also present as it is a problem that has been growing for a long time. Diagnosis and treatment are difficult to obtain as there are not enough specialists on the matter, and mental health is not yet a common topic among the population. Specialists in varied areas have proposed multiple solutions for the detection of the risk of depression; the computer science field has proposed some, based on language use and the data obtained through social media. Those solutions are mainly focused on objective features like n-grams and lexicons. We propose a contrast pattern-based classifier for detecting depression by using a new data representation based only on sentiment and emotion analysis extracted from post on social networks. The representation contains 28 different features which include information on sentiment, emotion, polarity, sarcasm, and other subjective information of the text. We then used a classifier that has not been used before in the state-of-the-art and obtained an AUC between 0.71 and 0.72. Finally we reproduced state-of-the-art models and statistically compared them with the result of the proposed model. The results show no significant statistical difference with a reproduction of the models found in the state-of-the-art. Furthermore, with the classifier used we were able to provide an explanation close to the language of an expert on the decision of the classifier.

https://orcid.org/0000-0002-6910-5922