Topic Mining and Sentiment Analysis based on the Online Review from Mooc Platform’s Logistics Course
Bibliographic Data
| ID | 16913383 |
|---|---|
| Authors | Yanling Zheng (0000-0001-9003-3153, Guilin University of Aerospace Technology), Minling Zeng (0000-0002-7137-7361, Institute of Software, corresponding author), Baiding Deng (Institute of Software), Hou Bin (0000-0003-0086-1567, Hunan University of Technology), Fei Liu (0000-0003-0967-9112), Liu Fei (0000-0001-7603-9436, Hunan University of Technology), Xuejun Lin (Jinan University) |
| Year | 2024 |
| Volume | 14 |
| Issue | 4 |
| Publication date | 2024-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | SAGE Open (JOURNAL) |
| Journal identifiers | ISSN: 2158-2440 • E-ISSN: 2158-2440 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/21582440241303486 |
| OpenAlex | W4405133215 |
| Language | EN |
| Citations received | 1 |
| References cited | 30 |
In the context of ongoing curriculum teaching reform accelerated by the COVID-19 pandemic, evaluating online logistics courses from the learners’ perspectives and identifying strategies for improvement have become critical areas of inquiry. Despite the growing body of literature on online education, existing studies often overlook the specific topics of interest and emotional tendencies of online logistics course learners. This study addresses this gap by utilizing the push-pull-mooring (PPM) theory, originally developed for human migration studies, to elucidate the transition of learners from offline to online environments, while exploring the formation process and significance of online logistics course instruction. We employ the ROST Content Mining System (ROST CM) software to conduct word frequency analysis, semantic network analysis, and sentiment analysis on review text data from two logistics courses offered on the China University massive open online courses (MOOC) platform. Our findings indicate that online learners prioritize three key aspects: “teacher,”“course,” and “learning content,” with high-frequency words revealing a hierarchical structure of “topic → transition → emotion.” While most learners expressed positive and satisfactory sentiments, a minority conveyed neutral or negative emotions. Based on these insights, this paper proposes targeted recommendations focusing on instructor guidance, enhancing online interaction, and fostering reflective teaching practices
Data science · Massive open online course · Mathematics education · Natural language processing · Online course · Sentiment analysis · World Wide Web · Computer Science · Diverse Approaches in Healthcare and Education Studies · Educational Systems and Policies · Engineering · Psychology · Technology and Data Analysis
"Migrating" to New Service Providers
Design and validation of a diagnostic Mooc evaluation method combining AHP and text mining algorithms
Online learning during the Covid-19 pandemic
Paradigms in migration research
Travelers' switching behavior in the airline industry from the perspective of the push-pull-mooring framework
A theory of migration
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |