Conference Papers
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Item Impact of Stress During COVID-19 Pandemic(Institute of Electrical and Electronics Engineers Inc., 2023) Angalakuditi, H.; Bhowmik, B.The COVID-19 pandemic has affected our lives in many ways. Many people faced different challenges during the pandemic to accomplish their daily activities. Many people faced various challenges during the pandemic might have been very stressful, overwhelming, and disgusting. Therefore, it is common to feel stress, irritation, mood swings, and anxiety during the pandemic. Different methodologies by medical practitioners are being taken. Additionally, researchers from academia are also trying to strengthen the methods. Unfortunately, the way for automatic, continuous, and invisible stress detection by the researchers are insufficient and not studied in depth. It becomes essential in the post-pandemic scenario due to COVID-19 disease. This paper studies the impact of stress on people during the COVID-19 pandemic. The study includes origin, classification, impact on health, prevention solutions, etc. Further statistics on the affected people by the stress during the period are provided. © 2023 IEEE.Item Exploring Depression Symptoms through Similarity Methods in Social Media Posts(CEUR-WS, 2023) Recharla, N.; Bolimera, P.; Gupta, Y.; Anand Kumar, M.Regardless of age, gender, or color, depression affects people all over the world. People feel increasingly at ease sharing their opinions on social networking sites practically every day in the present era of communication and technology. Reddit is a social networking site consisting of subreddits, or single-topic communities, created, maintained, and frequented by anonymous users. Users have the ability to post, comment on, and reply to posts within subreddits. Data for this suggested model is gathered from user posts on Reddit. Our approach involves ranking sentences from a collection of Reddit posts according to their relevance to a depression symptom for the 21 symptoms of depression from the BDI-II Questionnaire. © 2023 Copyright for this paper by its authors.Item Stress Detection Using Deep Learning Algorithms(Institute of Electrical and Electronics Engineers Inc., 2023) Angalakuditi, H.; Bhowmik, B.Stress has become a prevalent issue in modern society, with various negative impacts on mental and physical health. Stress in people is a physiological and psychological reaction to an imagined threat or difficulty. Several things, including employment, relationships, income, health problems, and significant life transitions, can cause stress. Depending on the person and the circumstance, stress symptoms can vary. They frequently include emotions of worry, irritation, and restlessness, as well as physical symptoms like headaches and muscle strain. Early stress detection is crucial for effective intervention and prevention of stress-related health issues. Detecting stress in real-time can be valuable in various domains such as healthcare, mental health, human-computer interaction, and workplace performance. This paper proposes a method for detecting stress using deep learning. A set of pre-trained models are employed for stress detection. The proposed technique is evaluated with publicly available datasets. Experimented results showed that the proposed stress detection method achieves accuracy in the range of 85.71-97.50% and the loss ranging from 0.4061 to 1.8144. © 2023 IEEE.Item Depression Severity Detection from Social Media Posts(Springer Science and Business Media Deutschland GmbH, 2024) Recharla, N.; Bolimera, P.; Gupta, Y.; Anand Kumar, M.A.Regardless of age, gender, or color, mental health problems affect people all over the world. People feel increasingly at ease sharing their opinions on social networking sites (SNS) practically every day in the present era of communication and technology. Reddit is a social networking site that consists of subreddits, or single-topic communities, that are created, maintained, and frequented by anonymous users. The dataset used in the paper is, eRisk2021 dataset provided for task 3, which is used for depression severity measurement. It consists posts of Reddit users. In this paper, the approach involves finding user depression severity based on their Reddit history with the help of the BDI-II questionnaire, which is discussed. The paper provides three different approaches in finding the users depression severity from their social media data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Stratification of Depressed and Non-Depressed Texts from Social Media using LSTM and its Variants(Elsevier B.V., 2024) Keerthan Kumar, T.G.K.; Anoop, R.; Koolagudi, S.G.; Rao, T.; Kodipalli, A.This work examines the performance of various LSTM (long short-term memory) variants on social media text data. This study evaluates the performance of LSTM models with different architectures, namely, classic LSTM, Bidirectional LSTM, Stacked LSTM, gated recurrent unit (GRU), and bidirectional GRU, on a social network dataset comprising texts extracted from multiple social media platforms. We aim to identify the most effective LSTM variant of the five considered LSTM models for text analysis through a comparative study of the models' precision, recall, F1-score, and accuracy. The research findings show that the Classic LSTM and the GRU model perform better than the other models in accuracy. In contrast, the bidirectional models (Bidirectional LSTM and Bidirectional GRU) provide better precision scores than their respective primitive models. This research has significant implications for developing more efficient models for natural language processing applications. It offers beneficial insights into the implications involving the scrutiny of depression on social media platforms through text data analysis. © 2024 Elsevier B.V.. All rights reserved.