arXiv subjects

Muskan Garg

Publications and source records attributed to Muskan Garg.

20 records · Page 2Linked to original sources

arXiv2022

CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts

Research community has witnessed substantial growth in the detection of mental health issues and their associated reasons from analysis of social media. We introduce a new dataset for Causal Analysis of Mental health issues in Social media posts (CAMS). Our contributions for causal analysis are two-fold: causal interpretation and causal categorization. We introduce an annotation schema for this task of causal analysis. We demonstrate the efficacy of our schema on two different datasets: (i) crawling and annotating 3155 Reddit posts and (ii) re-annotating the publicly available SDCNL dataset of 1896 instances for interpretable causal analysis. We further combine these into the CAMS dataset and make this resource publicly available along with associated source code: https://github.com/drmuskangarg/CAMS. We present experimental results of models learned from CAMS dataset and demonstrate that a classic Logistic Regression model outperforms the next best (CNN-LSTM) model by 4.9\% accuracy.

cs.CL↗

arXiv2021

Data Augmentation for Mental Health Classification on Social Media

The mental disorder of online users is determined using social media posts. The major challenge in this domain is to avail the ethical clearance for using the user generated text on social media platforms. Academic re searchers identified the problem of insufficient and unlabeled data for mental health classification. To handle this issue, we have studied the effect of data augmentation techniques on domain specific user generated text for mental health classification. Among the existing well established data augmentation techniques, we have identified Easy Data Augmentation (EDA), conditional BERT, and Back Translation (BT) as the potential techniques for generating additional text to improve the performance of classifiers. Further, three different classifiers Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR) are employed for analyzing the impact of data augmentation on two publicly available social media datasets. The experiments mental results show significant improvements in classifiers performance when trained on the augmented data.

cs.CL↗