Flamingos_python@ LT - EDI -2023: An Ensemble Model to Detect Severity of Depression (2023)

Abstract: The prevalence of depression is increasing globally, and there is a need for effective screening and detection tools. Social media platforms offer a rich source of data for mental health research. The paper aims to detect the signs of depression of a person from their social media postings wherein people share their feelings and emotions. The task is to create a system that, given social media posts in English, should classify the level of depression as ‘not depressed’, ‘moderately depressed’ or ‘severely depressed’. The paper presents the solution for the Shared Task on Detecting Signs of Depression from Social Media Text at LT-EDI@RANLP 2023. The proposed system aims to develop a machine learning model using machine learning algorithms like SVM, Random forest and Naive Bayes to detect signs of depression from social media text. The model is trained on a dataset of social media posts to detect the level of depression of the individuals as ‘not depressed’, ‘moderately depressed’ or ‘severely depressed’. The dataset is pre-processed to remove duplicates and irrelevant features, and then, feature engineering techniques is used to extract meaningful features from the text data. The model is trained on these features to classify the text into the three categories. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score. The ensemble model is used to combine these algorithms which gives accuracy of 90.2% and the F1 score is 0.90. The results of the proposed approach could potentially aid in the early detection and prevention of depression for individuals who may be at risk.

## introduction depression is a mood disorder that causes a persistent feeling of sadness and loss of interest. it is also called major depressive disorder or clinical depression, it affects how one feel, think and behave and can lead to a variety of emotional and physical problems. detecting depression is important since it has to be observed and treated at an early stage to avoid severe consequences. it aims to detect the signs of depression of a person from their social media postings wherein people share their feelings and emotions. social media platforms have emerged as a valuable source of data for mental health research. they provide an opportunity to study the language and behaviour patterns of individuals, which may reveal early signs of depression. given social media postings in english, the system should classify the signs of depression into three labels namely "not depressed", "moderately depressed", and "severely depressed". this project aims to develop a machine learning model using support vector machine (svm), random forest and naive bayes algorithm to detect signs of depression from social media text. ## related works a depression recognition method for college students @xcite proposed by yan ding et al., had used a deep integrated support vector machine (disvm) algorithm to classify the input data, and finally realize the recognition of depression. wolohan et al., (2018) created a dataset based on reddit posts in which users were assigned to one group: depressed or control. a transformers approach to detect depression in social media @xcite by malviya et al., had analyzed the posts using the linguistic inquiry and word count tool (liwc). findings of the shared task on detecting signs of depression from social media @xcite had used a variety of technologies from traditional machine learning algorithms to deep learning models. scubemsec@lt-edi-acl2022: detection of depression using transformer models @xcite by s, sivamanikandan et al., used different transformer models like distilbert, roberta and albert to detect depression which had achieved a macro f1 score of 0.337, 0.457 and text data label like no matter how much sleep i get always fatigued. not depression all i wanna do right now is crawl out of myself. does that make sense? moderate a few anxiety attacks and i'm ready to go severe ## dataset the dataset used by the proposed approach consists of social media text posts that have been annotated with labels indicating the presence or absence of signs of depression. specifically, the labels are 'not depressed', 'moderately depressed' or 'severely depressed'. the dataset has been collected from a variety of social media platforms and contains text data in english. example texts with labels from the dataset are presented in bagging and random forest: bagging is an ensemble algorithm that fits multiple models on different subsets of a training dataset, then combines the predictions from all models. random forest is an extension of bagging that also randomly selects subsets of features used in each data sample. both bagging and random forests have proven effective on a wide range of different predictive modeling problems. randomness injection: random values in machine learning are derived by random number generators. to create random values, the generator is first initialized with a seed, a number that represents the starting point for the random number generation. the generator then creates random values from that starting point with a specific algorithm. feature-selection ensembles: the idea behind ensemble feature selection is to combine multiple different feature selection methods, taking into account their strengths, and create an optimal best subset. in general, it makes a better feature space and reduces the risk of choosing an unstable subset. error-correcting output coding: the error-correcting output coding method is a technique that allows a multi-class classification problem to be reframed as multiple binary classification problems, allowing the use of native binary classification models to be used directly. ## conclusion the proposed system aim to detect severity of depression from social media text using machine figure 3: svm experimentation was conducted using three machine learning algorithms, namely svm, random forest and naive bayes, and evaluated their performance using various metrics like accuracy, precision, recall, and f1-score. by using ensemble model the accuracy obtained is 47.7% and the f1 score is 0.262. overall, the proposed system demonstrate the potential of machine learning techniques to detect severity of depression from social media text, which can aid in early detection and intervention of depression.