Shabir Hussain¹, Muhammad Ayoub², Yang Yu¹, Junaid Abdul Wahid¹, Akmal Khan³, Dietmar P. F. Moller⁴, Hou Weiyan^1,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5355-5377, 2023, DOI:10.32604/cmc.2023.036779 - 29 April 2023

Abstract As the COVID-19 pandemic swept the globe, social media platforms became an essential source of information and communication for many. International students, particularly, turned to Twitter to express their struggles and hardships during this difficult time. To better understand the sentiments and experiences of these international students, we developed the Situational Aspect-Based Annotation and Classification (SABAC) text mining framework. This framework uses a three-layer approach, combining baseline Deep Learning (DL) models with Machine Learning (ML) models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 dataset. Using the… More >

Mian Xia^1,2,3,*, Changming Duan⁴

International Journal of Mental Health Promotion, Vol.22, No.3, pp. 159-172, 2020, DOI:10.32604/IJMHP.2020.011117 - 14 August 2020

Abstract The purpose of this study was to investigate the stress and coping strategy of Chinese International Students (CISs) during the epidemic, and to explore the relationship between identification with Chinese cultural beliefs and coping strategies, positive experience, negative emotion and need for psychological support. Two hundred and thirty CISs participated in the study by filling out a series of questionnaires that we designed. The series of questionnaires contained six scales: Pandemic Related Stress (PRS), Identification with Chinese Cultural Beliefs (ICCB), Stress Coping Strategy (SCS), Positive Experience (PE) and Negative Emotion (NE) and Need for Psychological… More >