SSN _ MLRG 3 @ LT - EDI - ACL 2022-Depression Detection System from Social Media Text using Transformer Models (2022)

Abstract: Depression is a common mental illness that involves sadness and lack of interest in all day-to-day activities. The task is to classify the social media text as signs of depression into three labels namely “not depressed”, “moderately depressed”, and “severely depressed”. We have build a system using Deep Learning Model “Transformers”. Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. The multi-class classification model used in our system is based on the ALBERT model. In the shared task ACL 2022, Our team SSN_MLRG3 obtained a Macro F1 score of 0.473.

## introduction social media is developed as a great point for its users to communicate with their friends, relatives and share their opinions, photos, and videos reflecting their feelings and sentiments. this creates an opportunity to analyze social media data for user's feelings and sentiments to investigate their moods and attitudes when they are communicating through the social media apps. depression is the common issue of today's youngsters and suicide due to depression is growing day by day. people often communicate their moods through tweets or messages but people around them fail to understand the underlying truth behind the words. @xcite conducted depression survey among 216 undergraduate students with real time internet data. feuston and piper (2018) analyzed instagram posts, pictures, captions and concluded that mental health and illness are interrelated through the application of the coded gaze. the task 4 in second workshop on language technology for equality, diversity, inclusion (lt-edi-2022) @xcite was conducted to detect the signs of depression from social media text in english language. we tried to classify each message as "not depressed", "moderately depressed", and "severely depressed". the training set provided by the organizers contains 8,891 social media messages. the given dataset is used to train our model. ## related works in last five years, the use of social media has increased drastically and the data available too, has increased. hence, numerous studies on emotion analysis and depression analysis have been carried out in recent times. most of them revolve around machine learning and deep learning techniques. @xcite showcased how bidirectional encoder representations from transformers (bert) @xcite models can be used for text summarization. they proposed a general framework for extractive and abstractive models. this helped us to understand the bert encoderdecoder architecture. @xcite carried out work on detecting suicidality on twitter using support vector machine (svm) and logistic regression with crossvalidation methods. svm-tf-idf filter algorithm showed best results with combined dataset accuracy of 76%. it stated that more searches on suicide related terms can improve the accuracy of the model. @xcite built an emotion recognition system using speech features and transcriptions. different deep neural network (dnn) architectures were used among which text-mfcc(mel frequency cepstral coefficients) gave an accuracy of 76.1%. shah et al. (2020) used deep learning based models for analyzing the depression state. they tried different combinations of metadata features and word embedding techniques with bidirectional long short term memory (bilstm). among different features, word2vecembed+meta features performed well with a f1 score of 81%. we have worked in contextual emotion and sentiment analysis with various machine learning and gaussian process models in @xcite , @xcite , @xcite , @xcite , @xcite and ( rajendram et al., 2017a ) which form the base for dealing with emotions and kindle our interest in depression detection. ## methodology and data the task is to discover the mood of the user from the social media posts and it is always difficult to extract the emotions from the text. a post can have different combination of emotions. the architecture diagram for the depression classification is shown in figure 1 . the training dataset is preprocessed to remove the unwanted information and is given to albert model to learn the features. test data is given to the built model to classify the text into 3 states of depression. ## result we have used evaluation metrics as accuracy, macro f1-score, macro recall, macro precision, weighted f1-score, weighted recall and weighted precision. the performance is shown in we obtained 20 th rank with an accuracy of 57% while the top ranked team obtained 66% as accuracy. due to the resource constraints we trained ## conclusion we have built albert base model for the task to detecting signs of depression from social media posts. all the models are preprocessed with nltk, which we think is an important factor for building a good model. the emotion of a social media posts depends on individual's perception and cannot be judged by simple conventional models. this is one of the reason for the reduced accuracy. understanding one's feelings and mood is too delicate for models to detect them accurately. imbalanced data distribution among the output class labels can be another reason for less accuracy. the training data has high number of moderately depressed posts followed by not depressed and severely depressed.