D epression O ne@ LT - EDI - ACL 2022: Using Machine Learning with SMOTE and Random U nder S ampling to Detect Signs of Depression on Social Media Text. (2022)

Abstract: Depression is a common and serious medical illness that negatively affects how you feel, the way you think, and how you act. Detecting depression is essential as it must be treated early to avoid painful consequences. Nowadays, people are broadcasting how they feel via posts and comments. Using social media, we can extract many comments related to depression and use NLP techniques to train and detect depression. This work presents the submission of the DepressionOne team at LT-EDI-2022 for the shared task, detecting signs of depression from social media text. The depression data is small and unbalanced. Thus, we have used oversampling and undersampling methods such as SMOTE and RandomUnderSampler to represent the data. Later, we used machine learning methods to train and detect the signs of depression.

## introduction according to psychiatry, depression is defined as a mental condition characterized by severe despondency and dejection, typically also with feelings of inadequacy and guilt, often accompanied by lack of energy and disturbance of appetite and sleep. depression remains a significant issue worldwide, and often it progresses to suicidal intention if left undetected @xcite . thus the diagnosis of depression is an important task. many existing methods for detecting depression rely on electronic health records or suicide notes. but such data is limited and challenging to acquire. in the current generation, online forums on social media act as a means where people vent out how they feel. we can scrape these resources to create datasets. such data, if annotated, can be helpful to detect depression @xcite . a growing number of studies are using such data for research and diagnostic purposes. a survey on detecting depression using social media data is given in the paper @xcite . detecting depression represents a significant clinical challenge, both for the advancement of how depression is treated and for implementing interventions @xcite . to encourage work on depression from social media comments/posts, the lt-edi community has organized a shared task to identify the signs of depression of a person from their social media postings where people share their feelings and emotions @xcite . the dataset used for this task has a total of 16,632 train, valid, and test comments in the english language. this task aims to classify the given depression data into three classes, severe, moderate, and not_depression. this paper presents a method for detecting/classifying depression text. we have used under sampling and oversampling to represent the data better. then we used a machine learning classifier to train and classify the given text. the paper is organized as follows. section 2 provides related work on depression detection on social media text. section 3 provides information on the task and datasets. section 4 describes the our submission. section 5 presents the experimental setup and the performance of the model. section 6 concludes our work. ## literature survey this section provides a brief of research done till now on depression detection. the given depression dataset has long sequences, and the bert models could not process long sequences. also, text summarization techniques are not 100% accurate and will be propagated to bert models. thus we opted for the svm and rnn models for depression detection. ## task setup the goal of this task is to detect depression from social media. the model should classify the signs of depression into three labels, namely "not depressed", "moderate", and "severe". the dataset has 16,632 comments, wherein 8,891 belong to the training set, 4,496 belong to validation, and 3,245 belong to the test set. all the posts are in the english language. as given in figure 1 , we can see that there are more instances of moderate classes when compared to the not-depressed and severe classes in the given data. also, the data has a wide range of sentence lengths as given in figure 2 . we have also observed that more than 6% of the sentences are long 1 . the long sentences are not suitable for the bert model as it only works if the tokens are less than 512. so we chose robust classification algorithms such as svm to classify the data and detect depression in the dataset. 4 our submission as mentioned above, there are more instances of moderate class labels when compared to notdepressed and severe. it leads to an imbalance in data. so we chose the resampling method. resampling involves creating a new altered version of the training dataset, in which the selected examples have a similar class distribution. the simple way is to choose instances for the transformed dataset randomly. thus it is called random resampling. it is a simple and effective strategy to handle imbalanced classification problems. the two main methods of random resampling are oversampling and undersampling. random oversampling random oversampling involves randomly selecting examples from the minority class, with replacement, and adding them to the training dataset. random undersampling random undersampling involves randomly selecting examples from the majority class and deleting them from the training dataset until a more balanced distribution is reached. this technique is practical where the skewed distribution affects the classification models, and multiple examples for a given class can overfit the model. it makes the model to be biased towards the class that has the majority of instances. if we only use random undersampling for the given majority class, i.e., moderate, then the data @xcite suggests that, using random undersampling to trim the number of in the majority class, then use smote to oversample the minority class to balance the class distribution. the combination of smote and under-sampling performs better than plain under-sampling. after resampling the classes, we have used an svm classifier to train the transformed data and applied the model to the test set. ## experiments the section presents the baselines, hyper-parameter settings, and analysis of observed results.