Pain Assessment Using Facial Action Units and Bayesian Network

Guo, Wenqiang; Xu, Ziwei; Guo, Zhiwei; Mao, Lingling; Hou, Yuexian; Huang, Zixuan

doi:10.23919/ccc52363.2021.9550304

Cited by 2 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed model utilised a CNN for feature extraction and a long short-term memory (LSTM) network for sequence modelling. Another study proposed a method for facial sentiment analysis that utilised a set of hand-crafted features, including facial action units and their combinations, to train an SVM classifier [69].…”

Section: Levels Of Facial Sentiment Analysismentioning

confidence: 99%

Emotions Matter: A Systematic Review and Meta-Analysis of the Detection and Classification of Students’ Emotions in STEM during Online Learning

Anwar,

Rehman,

Nasralla

et al. 2023

Education Sciences

View full text Add to dashboard Cite

In recent years, the rapid growth of online learning has highlighted the need for effective methods to monitor and improve student experiences. Emotions play a crucial role in shaping students’ engagement, motivation, and satisfaction in online learning environments, particularly in complex STEM subjects. In this context, sentiment analysis has emerged as a promising tool to detect and classify emotions expressed in textual and visual forms. This study offers an extensive literature review using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) technique on the role of sentiment analysis in student satisfaction and online learning in STEM subjects. The review analyses the applicability, challenges, and limitations of text- and facial-based sentiment analysis techniques in educational settings by reviewing 57 peer-reviewed research articles out of 236 articles, published between 2015 and 2023, initially identified through a comprehensive search strategy. Through an extensive search and scrutiny process, these articles were selected based on their relevance and contribution to the topic. The review’s findings indicate that sentiment analysis holds significant potential for improving student experiences, encouraging personalised learning, and promoting satisfaction in the online learning environment. Educators and administrators can gain valuable insights into students’ emotions and perceptions by employing computational techniques to analyse and interpret emotions expressed in text and facial expressions. However, the review also identifies several challenges and limitations associated with sentiment analysis in educational settings. These challenges include the need for accurate emotion detection and interpretation, addressing cultural and linguistic variations, ensuring data privacy and ethics, and a reliance on high-quality data sources. Despite these challenges, the review highlights the immense potential of sentiment analysis in transforming online learning experiences in STEM subjects and recommends further research and development in this area.

show abstract

Section: Levels Of Facial Sentiment Analysismentioning

confidence: 99%