Variants of Long Short-Term Memory for Sentiment Analysis on Vietnamese Students’ Feedback Corpus

Nguyen, Vu; Nguyen, Kiet Van; Nguyen, Ngan Luu-Thuy

doi:10.1109/kse.2018.8573351

Cited by 30 publications

(18 citation statements)

References 14 publications

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“…In order to have a comprehensive view in this research, we will compare traditional machine learning with deep learning on this dataset as the previous work [17]. Besides, we will study the LSTM variants as the study [33] has introduced.…”

Section: Discussionmentioning

confidence: 99%

Job Prediction: From Deep Neural Network Models to Applications

Huynh¹,

Nguyen²,

Nguyen³

et al. 2020

2020 RIVF International Conference on Computing and Communication Technologies (RIVF)

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Determining the job is suitable for a student or a person looking for work based on their job descriptions such as knowledge and skills that are difficult, as well as how employers must find ways to choose the candidates that match the job they require. In this paper, we focus on studying the job prediction using different deep neural network models including TextCNN, Bi-GRU-LSTM-CNN, and Bi-GRU-CNN with various pre-trained word embeddings on the IT job dataset. In addition, we proposed a simple and effective ensemble model combining different deep neural network models. Our experimental results illustrated that our proposed ensemble model achieved the highest result with an F1-score of 72.71%. Moreover, we analyze these experimental results to have insights about this problem to find better solutions in the future.

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Section: Discussionmentioning

confidence: 99%

Job Prediction: From Deep Neural Network Models to Applications

Huynh¹,

Nguyen²,

Nguyen³

et al. 2020

2020 RIVF International Conference on Computing and Communication Technologies (RIVF)

Self Cite

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“…From the learners' perspective, there are a number of papers [10][11][12] that have applied sentiment analysis to investigate the correlation of attitude and performance with learners' sentiments as well as the relationship between learners' sentiments and drop-out rates in Massive Open Online Courses (MOOCs). Regarding teachers' perspectives, sentiment analysis has been widely adopted by researchers [13][14][15] to examine various teacherassociated aspects expressed in students' reviews or comments in discussion forums.…”

Section: Overview Of Sentiment Analysismentioning

confidence: 99%

“…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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“…In Vietnamese, there are quite a quantity of research works on other NLP tasks such as parsing [26,27], part-of-speech [28,29], named entity recognition [30,31], sentiment analysis [16,17,18] and question answering [32,33]. However, there are no research publications on emotion recognition for Vietnamese social media texts.…”

Section: Related Workmentioning

confidence: 99%

Emotion Recognition for Vietnamese Social Media Text

Ho¹,

Nguyen²,

Nguyen³

et al. 2019

Preprint

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Emotion recognition or emotion prediction is a higher approach or a special case of sentiment analysis. In this task, the result is not produced in terms of either polarity: positive or negative or in the form of rating (from 1 to 5) but of a more detailed level of analysis in which the results are depicted in more expressions like sadness, enjoyment, anger, disgust, fear and surprise. Emotion recognition plays a critical role in measuring brand value of a product by recognizing specific emotions of customers' comments. In this study, we have achieved two targets. First and foremost, we built a standard Vietnamese Social Media Emotion Corpus (UIT-VSMEC) with exactly 6,927 emotionannotated sentences, contributing to emotion recognition research in Vietnamese which is a low-resource language in natural language processing (NLP). Secondly, we assessed and measured machine learning and deep neural network models on our UIT-VSMEC corpus. As a result, the CNN model achieved the highest performance with the weighted F1-score of 59.74%. Our corpus is available at our research website 1 .

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Variants of Long Short-Term Memory for Sentiment Analysis on Vietnamese Students’ Feedback Corpus

Cited by 30 publications

References 14 publications

Job Prediction: From Deep Neural Network Models to Applications

Job Prediction: From Deep Neural Network Models to Applications

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

Emotion Recognition for Vietnamese Social Media Text

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