DLRG @ LT - EDI - ACL 2022:Detecting signs of Depression from Social Media using XGB oost Method (2022)

Abstract: Depression is linked to the development of dementia. Cognitive functions such as thinkingand remembering generally deteriorate in dementiapatients. Social media usage has beenincreased among the people in recent days. Thetechnology advancements help the communityto express their views publicly. Analysing thesigns of depression from texts has become animportant area of research now, as it helps toidentify this kind of mental disorders among thepeople from their social media posts. As part ofthe shared task on detecting signs of depressionfrom social media text, a dataset has been providedby the organizers (Sampath et al.). Weapplied different machine learning techniquessuch as Support Vector Machine, Random Forestand XGBoost classifier to classify the signsof depression. Experimental results revealedthat, the XGBoost model outperformed othermodels with the highest classification accuracyof 0.61% and an Macro F1 score of 0.54.

## introduction depression is a risk factor for dementia. dementia patients often experience a deterioration in cognitive abilities such as thinking and remembering @xcite . early detection and treatment of depressive symptoms can greatly improve the chances of controlling depression and reducing the harmful effects of depression on a person's well-being, health, and social-economic life. the task of distinguishing between depressed and nondepressed people using online social media is critical. information, communication, and posts on social media describe a user's emotional state @xcite . their sentimental state, on the other hand, will be strong, which could lead to a misdiagnosis of depression. clinical interviews and questionnaire surveys conducted by hospitals or organizations, where psychiatric assessment tables are used to determine mental disorder prognosis, are currently the most common procedures used. depression affects more than 300 million people worldwide, according to the world health organization. depression can have a negative impact on one's personal well-being, family life, and educational institutions at work, as well as contribute to physical damage. recently, the task of detecting depression in an earlier stage is attempted by researchers in alternative ways too. one such attempt is to mine the social media posts of people, from which the signs of depression can be detected. to this end, various machine learning techniques could be applied to diagnose depression from feelings or emotions expressed in social media texts, by using artificial intelligence (ai) and natural language processing (nlp)-based approaches. we have extracted features from the text and bow method is appliled. for building the model, we have used svm and ensmeble based methods random forest classifier and xgb classifier. from the experimental results, we found that, xgb outperforms other methods with an accuarcy of 0.60 and an f 1 score of 0.54. ## literature review ibitoye a. @xcite looked at two research that looked at how supervised machinelearning classifiers could predict the interaction of emotions. they used classification methods to classify depression-related messages on social media. mathur (mathur et al., 2020) suggested a strategy based on a bidirectional lstm (blstm) + attention model for detecting depression early in twitter users' messages. to detect depression from the twitter dataset, orabi @xcite suggested using the continuous bag of words (cbow) embedding approach. kim jin et al. @xcite researched about how a supervised machine learning algorithm can help detect post-traumatic stress disorder by measuring predicting parameters. ## methodology the overall workflow of the proposed system is depicted in figure 1 the flow depicts data preprocessing, lemmatization followed by training and testing using machine learning model. support vector machine, random forest, and xgboost are utilized to classify depression from text data into no depression, severe and moderate depression. ## experimental study and results discussion the implementation work for the depression detection challenge is described in this section. the dataset and data acquisition procedure are explained in section 4.1. the division of data for training and testing purposes is briefly explained in section 4.2. section 4.3 describes the results attained from the models. ## conclusion early detection and treatment of depressive symptom improves a person's chances of controlling depression and reducing its harmful effects on their well-being, health, and social-economic life. the dataset comprises a text data set for categorising depression into three categories: moderate, severe, and not depressed. machine learning algorithms are used to classify the text data for identifying the depression data. using svm and random forest classifiers resulted in an accuracy of 0.55 and 0.43. the highest classification accuracy of 0.60 was achieved xgboost classifier.