Public opinion on MOOCs: sentiment and content analyses of Chinese microblogging data

Zhou, Mingming

doi:10.1080/0144929x.2020.1812721

Cited by 14 publications

(6 citation statements)

References 75 publications

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“…Based on quantitative words’ frequency and co-occurrence analysis, comparative keywords analysis, and structural topic modeling, this study mined and visualized 69,232 course reviews from Chinese LMOOCs learners. The results revealed that students’ overall perception and attitude were positive, which was consistent with previous findings [ 19 , 23 ]. Most of the comments suggested that courses are good, suitable, and supportive for their language learning.…”

Section: Discussionsupporting

confidence: 91%

“…Such data can be used to inform decisions about course design, instructor selection, and future curricular offerings and has become mainstream since the year 2018. For instance, general MOOCs researchers have frequently conducted sentiment analysis and topic modeling on comments from discussion forums [ 16 ], course reviews [ 17 , 18 ], and public responses to MOOCs on social media [ 19 ]. However, LMOOCs researchers prefer to use qualitative content analysis to explore learners’ subjective evaluation.…”

Section: Introductionmentioning

confidence: 99%

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Mining and visualizing large-scale course reviews of LMOOCs learners through structural topic model

Yang

2023

PLoS ONE

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Understanding Language Massive Online Open Courses (LMOOCs) learners’ subjective evaluation is essential for language teachers to improve their instructional design, examine the teaching and learning effects, and promote course quality. The present research uses word frequency and co-occurrence analysis, comparative keyword analysis, and structural topic modeling to analyze 69,232 reviews from one Massive Online Open Courses (MOOCs) platform in China. Learners hold a strongly positive overall perception of LMOOCs. Four negative topics appear more commonly in negative reviews as compared to positive ones. Additionally, variations in negative reviews across course types are examined, indicating that learners’ main concerns about high-level LMOOCs include teaching/learning problems, learner expectation, and learner attitude, whereas learners of low-level courses are more critical in the topic of scholarship ability. Our study contributes to the LMOOCs study by providing a better understanding of learners’ perceptions using rigorous statistical techniques.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Mining and visualizing large-scale course reviews of LMOOCs learners through structural topic model

Yang

2023

PLoS ONE

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“…In order to mine and examine the public attitudes of the Chinese people toward live-streaming fitness, this study conducted both sentiment analysis and content analysis on the collected Weibo posts to monitor the public sentiment and trending opinions of the Chinese people on this fitness model. Sentiment analysis is a desirable method for opinion mining and subjectivity analysis ( 26 ), which applies to any textual form of opinion such as microblogs and online reviews ( 27 ). Previous studies have demonstrated that sentiment analysis can reveal behavioral and affective trends on public health issues.…”

Section: Methodsmentioning

confidence: 99%

Exploring public attitudes toward live-streaming fitness in China: A sentiment and content analysis of China's social media Weibo

Tian

Yin²,

Gan³

2022

Front. Public Health

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ObjectivesLive-streaming fitness is perceived by the Chinese government as an invaluable means to reduce the prevalence of physical inactivity amid the COVID-19 pandemic. This study aims to investigate the public altitudes of the Chinese people toward live-streaming fitness and provide future health communication strategies on the public promotion of live-streaming fitness accordingly.MethodsThis study collected live-streaming fitness-related microblog posts from July 2021 to June 2022 in Weibo, the Chinese equivalent to Twitter. We used the BiLSTM-CNN model to carry out the sentiment analysis, and the structured topic modeling (STM) method to conduct content analysis.ResultsThis study extracted 114,397 live-streaming fitness-related Weibo posts. Over 80% of the Weibo posts were positive during the period of the study, and over 85% were positive in half of the period. This study finds 8 topics through content analysis, which are fitness during quarantine; cost reduction; online community; celebrity effect; Industry; fitness injuries; live commerce and Zero Covid strategy.ConclusionsIt is discovered that the public attitudes toward live-streaming fitness were largely positive. Topics related to celebrity effect (5–11%), fitness injuries (8–16%), live commerce (5–9%) and Zero Covid strategy (16–26%) showed upward trends in negative views of the Chinese people. Specific health communication strategy suggestions are given to target each of the negative topics.

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“…Although often referred to as the Chinese equivalent of Twitter, Weibo is more like a mixture of features of Twitter and Facebook [25]. Weibo had 582 million monthly active users and 252 million daily active users by the first quarter of 2022 [26], and, as such, it is recognized as a unique platform to explore the attitudes of the Chinese people due to the sheer number of users [27]. Weibo has been widely used as a data source for mental health studies.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Factors Associated with Mental Health Attitude in China: A Structural Topic Modeling Approach

Yin

Tian

et al. 2022

IJERPH

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Mental health attitude has huge impacts on the improvement of mental health. In response to the ongoing damage the COVID-19 pandemic caused to the mental health of the Chinese people, this study aims to explore the factors associated with mental health attitude in China. To this end, we extract the key topics in mental health-related microblogs on Weibo, the Chinese equivalent of Twitter, using the structural topic modeling (STM) approach. An interaction term of sentiment polarity and time is put into the STM model to track the evolution of public sentiment towards the key topics over time. Through an in-depth analysis of 146,625 Weibo posts, this study captures 12 topics that are, in turn, classified into four factors as stigma (n = 54,559, 37.21%), mental health literacy (n = 32,199, 21.96%), public promotion (n = 30,747, 20.97%), and social support (n = 29,120, 19.86%). The results show that stigma is the primary factor inducing negative mental health attitudes in China as none of the topics related to this factor are considered positive. Mental health literacy, public promotion, and social support are the factors that could enhance positive attitudes towards mental health, since most of the topics related to these factors are identified as positive ones. The provision of tailored strategies for each of these factors could potentially improve the mental health attitudes of the Chinese people.

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Public opinion on MOOCs: sentiment and content analyses of Chinese microblogging data

Cited by 14 publications

References 75 publications

Mining and visualizing large-scale course reviews of LMOOCs learners through structural topic model

Mining and visualizing large-scale course reviews of LMOOCs learners through structural topic model

Exploring public attitudes toward live-streaming fitness in China: A sentiment and content analysis of China's social media Weibo

Exploring the Factors Associated with Mental Health Attitude in China: A Structural Topic Modeling Approach

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