Network-based sentiment analysis
A novel framework for exploring learners’ emotions in Moocs
Bibliographic Data
| ID | 22413621 |
|---|---|
| Authors | Zhu Su (0000-0002-4094-7261, National Engineering Research Center of Educational Big Data, Central China Normal University), Zhongyu Shao (National Engineering Research Center of Educational Big Data, Central China Normal University), Yafeng Li (0009-0001-7313-7652, National Engineering Research Center of Educational Big Data, Central China Normal University), Haiyu Wang (0000-0002-6712-3078, National Engineering Research Center of Educational Big Data, Central China Normal University), Weibing Deng (Central China Normal University, corresponding author) |
| Year | 2026 |
| Pages | 1-24 |
| Publication date | 2026-01-26 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Distance Education (JOURNAL) |
| Journal identifiers | ISSN: 0158-7919 • E-ISSN: 1475-0198 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/01587919.2026.2619592 |
| OpenAlex | W7125692593 |
| Language | EN |
| References cited | 56 |
Sentiment analysis is crucial in education, yet traditional lexicon-based methods struggle to capture nuanced emotions and group dynamics. We propose a network-based approach using a dataset of 9,873 forum posts from 1,919 MOOC learners. In our sentiment network, learners are nodes in an emotional state space, connected when their emotional distance—defined as the Manhattan distance between their emotion-score vectors—is below a threshold. Analysis reveals a sparse network with emotional variability and small-world characteristics, promoting local clustering and relatively efficient information flow. Learners with lower positive emotions often occupy structurally central positions in the network, characterized by higher degree and betweenness centrality, while high-positive-emotion groups show academic underperformance. Conversely, negative or confused emotions are correlated with better outcomes. Higher network entropy is linked to improved academic performance, indicating enhanced emotional processing. These findings offer new insights into emotional interactions and learning outcomes, suggesting future research on specific emotions and refined emotion analysis in education.
Computer-mediated communication · Distance education · Educational technology · Electronic learning · Higher education · Qualitative research · Emotion and Mood Recognition · Online Learning and Analytics · Sentiment Analysis and Opinion Mining
Artificial Intelligence in Education
Complex networks
Teacher–child relationships, classroom climate, and children’s social-emotional and academic development.
Sens
Interaction and learning engagement in online learning
Affect and engagement during small group instruction
Sentiment analysis in education research
Supporting learners' self-regulated learning in Massive Open Online Courses
Learning performance and behavioral patterns of online collaborative learning
Refining qualitative ethnographies using Epistemic Network Analysis
Using multilayer network analysis to detect the collaborative knowledge construction characteristics among learner groups with low, medium, and high levels of cognitive engagement
Learners' interaction patterns in asynchronous online discussions
The generation of student engagement as a cognition-affect-behaviour process in a Twitter learning experience
Are Moocs Promising Learning Environments
| Citation velocity | historical |
|---|---|
| Highly cited | No |