UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis

Nguyen, Kiet Van; Nguyen, Vu; Nguyen, Phu; Truong, Tham T. H.; Nguyen, Ngan Luu-Thuy

doi:10.1109/kse.2018.8573337

Cited by 48 publications

(26 citation statements)

References 11 publications

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“…Vietnamese understudy criticism is utilized in their work, in which students give their feelings through a short printed rundown. It incorporates positive, negative, and unbiased remarks of Van Nguyen et al [8].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period

Sirajudeen

Duraisamy

Haleema

et al. 2021

Intelligent Computing and Innovation on Data Science

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The COVID-19 pandemic situation had imposed unexpected changes in our day-to-day life, especially in terms of how we work and how we learn. Before COVID-19, e-learning methodologies were embraced only by inquisitive learners and by few corporate companies and educational institutions to disseminate knowledge through an alternative mode of content delivery. Although e-learning offers great flexibility to the learners to learn at their own convenient time and pace, by accessing the content via any smart device irrespective of their geographical location, it has got some limitations too due to which most of the teachers/students, as well as the educational institutions, prefer the traditional method of teaching/ learning over online learning. In this paper, exclusive sentiment analysis has been carried out to assess the perception of the students on the adoption of online learning using a dataset that has been collected during the pre-COVID-19 pandemic period. The results of the analysis demonstrate that online learning was not as popular and widely adopted among the student's fraternity as it is during this COVID-19 pandemic period.

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Section: Introductionmentioning

confidence: 99%

Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period

Sirajudeen

Duraisamy

Haleema

et al. 2021

Intelligent Computing and Innovation on Data Science

View full text Add to dashboard Cite

show abstract

“…Regarding to the Vietnamese language processing, there are quite a number of research works on other tasks such as parsing [50]- [52], part-of-speech [53], [54], named entity recognition [55]- [57], sentiment analysis [58]- [60], and question answering [61]- [63]. However, to the extent of our knowledge, there are no research publications on multiplechoice machine reading comprehension.…”

Section: B Mrc Methodsmentioning

confidence: 99%

Enhancing Lexical-Based Approach With External Knowledge for Vietnamese Multiple-Choice Machine Reading Comprehension

et al. 2020

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Although Vietnamese is the 17 th most popular native-speaker language a in the world, there are not many research studies on Vietnamese machine reading comprehension (MRC), the task of understanding a text and answering questions about it. One of the reasons is because of the lack of high-quality benchmark datasets for this task. In this work, we construct a dataset which consists of 2,783 pairs of multiple-choice questions and answers based on 417 Vietnamese texts which are commonly used for teaching reading comprehension for elementary school pupils. In addition, we propose a lexicalbased MRC method that utilizes semantic similarity measures and external knowledge sources to analyze questions and extract answers from the given text. We compare the performance of the proposed model with several baseline lexical-based and neural network-based models. Our proposed method achieves 61.81% by accuracy, which is 5.51% higher than the best baseline model. We also measure human performance on our dataset and find that there is a big gap between machine-model and human performances. This indicates that significant progress can be made on this task. The dataset is freely available on our website b for research purposes.

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“…Regarding the applied techniques, there were only two major categories of techniques used to conduct sentiment analysis in the education domain during 2015 and 2017: NLP and ML. The first efforts [12,32] towards applying DL were presented during 2018, as shown in Figure 9. Moreover, an increasing research pattern of DL application appeared in 2019 and 2020-especially during 2020, where an equal distribution of DL versus the other techniques can be observed.…”

Section: Rq4 In Which Bibliographical Sources Are the Metrics Published And What Are The Research Trends And Patterns?mentioning

confidence: 99%

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

et al. 2021

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In the last decade, sentiment analysis has been widely applied in many domains, including business, social networks and education. Particularly in the education domain, where dealing with and processing students’ opinions is a complicated task due to the nature of the language used by students and the large volume of information, the application of sentiment analysis is growing yet remains challenging. Several literature reviews reveal the state of the application of sentiment analysis in this domain from different perspectives and contexts. However, the body of literature is lacking a review that systematically classifies the research and results of the application of natural language processing (NLP), deep learning (DL), and machine learning (ML) solutions for sentiment analysis in the education domain. In this article, we present the results of a systematic mapping study to structure the published information available. We used a stepwise PRISMA framework to guide the search process and searched for studies conducted between 2015 and 2020 in the electronic research databases of the scientific literature. We identified 92 relevant studies out of 612 that were initially found on the sentiment analysis of students’ feedback in learning platform environments. The mapping results showed that, despite the identified challenges, the field is rapidly growing, especially regarding the application of DL, which is the most recent trend. We identified various aspects that need to be considered in order to contribute to the maturity of research and development in the field. Among these aspects, we highlighted the need of having structured datasets, standardized solutions and increased focus on emotional expression and detection.

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UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis

Cited by 48 publications

References 11 publications

Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period

Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period

Enhancing Lexical-Based Approach With External Knowledge for Vietnamese Multiple-Choice Machine Reading Comprehension

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

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