• Smart Media Journal
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• v.13 no.6
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• pp.35-43
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• 2024

During the post-pandemic period, the interest in foreign language learning surged, leading to increased usage of language-learning apps. With the rising demand for these apps, analyzing app reviews becomes essential, as they provide valuable insights into user experiences and suggestions for improvement. This research focuses on extracting insights into users' opinions, sentiments, and overall satisfaction from reviews of HelloTalk, one of the most renowned language-learning apps. We employed topic modeling and emotion analysis approaches to analyze reviews collected from the Google Play Store. Several experiments were conducted to evaluate the performance of sentiment classification models with different settings. In addition, we identified dominant emotions and topics within the app reviews using feature importance analysis. The experimental results show that the Random Forest model with topics and emotions outperforms other approaches in accuracy, recall, and F1 score. The findings reveal that topics emphasizing language learning and community interactions, as well as the use of language learning tools and the learning experience, are prominent. Moreover, the emotions of 'admiration' and 'annoyance' emerge as significant factors across all models. This research highlights that incorporating emotion scores into the model and utilizing a broader range of emotion labels enhances model performance.

• Journal of Information Technology Applications and Management
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• v.21 no.3
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• pp.53-64
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• 2014

It is important to keep learners' feeling positive during learning to enhance learning performance. According to flow theory,challenge-skill balance is a precondition for flow experience: Learners feel anxiety when the challenge of learning is higher than their ability, feel boredom when the challenge of learning is lower than learners' ability, and engage in flow status when the challenge of learning matches the learners' ability. However, the current empirical study reveals that emotions related to enjoyment may appear when the learners' skill is equal to or higher than the learning challenge. Nevertheless, boredom emotion may appear when learners perceive the courses are difficult but unimportant. These empirical survey results revealed the necessary of rethinking the appearance of boredom and enjoyment emotions in computer education.

• Journal of Industrial Convergence
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• v.21 no.11
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• pp.75-83
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• 2023

This study aimed to identify the effect of academic emotions, learning flow and perceived teaching presence for academic achievement of nursing students in an uncontacted online class learning in the convergence society. The participants were 127 nursing students; data were analyzed using t-test, ANOVA, pearson correlation, multiple regression. It was found that: nursing students showed higher score at academic emotions, higher learning flow, and higher perceived teaching presence, higher score for academic achievement. The main factors influencing academic achievement were academic emotions, learning flow and perceived teaching presence. They explained about 42.7% of the academic achievement. Therefore, when operating uncontacted online classes for nursing students, it is necessary to consider the factors of learners, the personal efforts of the instructor, and systematic support for strengthening the instructor's capabilities.

• Dementia and Neurocognitive Disorders
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• v.23 no.3
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• pp.146-160
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• 2024

Background and Purpose: The emotions of people at various stages of dementia need to be effectively utilized for prevention, early intervention, and care planning. With technology available for understanding and addressing the emotional needs of people, this study aims to develop speech emotion recognition (SER) technology to classify emotions for people at high risk of dementia. Methods: Speech samples from people at high risk of dementia were categorized into distinct emotions via human auditory assessment, the outcomes of which were annotated for guided deep-learning method. The architecture incorporated convolutional neural network, long short-term memory, attention layers, and Wav2Vec2, a novel feature extractor to develop automated speech-emotion recognition. Results: Twenty-seven kinds of Emotions were found in the speech of the participants. These emotions were grouped into 6 detailed emotions: happiness, interest, sadness, frustration, anger, and neutrality, and further into 3 basic emotions: positive, negative, and neutral. To improve algorithmic performance, multiple learning approaches were applied using different data sources-voice and text-and varying the number of emotions. Ultimately, a 2-stage algorithm-initial text-based classification followed by voice-based analysis-achieved the highest accuracy, reaching 70%. Conclusions: The diverse emotions identified in this study were attributed to the characteristics of the participants and the method of data collection. The speech of people at high risk of dementia to companion robots also explains the relatively low performance of the SER algorithm. Accordingly, this study suggests the systematic and comprehensive construction of a dataset from people with dementia.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.26-34
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• 2024

Most human emotions are conveyed through facial expressions, which represent the predominant source of emotional data. This research investigates the impact of crowds on human emotions by analysing facial expressions. It examines how crowd behaviour, face recognition technology, and deep learning algorithms contribute to understanding the emotional change according to different level of crowd. The study identifies common emotions expressed during congestion, differences between crowded and less crowded areas, changes in facial expressions over time. The findings can inform urban planning and crowd event management by providing insights for developing coping mechanisms for affected individuals. However, limitations and challenges in using reliable facial expression analysis are also discussed, including age and context-related differences.

• Asia pacific journal of information systems
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• v.34 no.2
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• pp.372-399
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• 2024

Online reviews play a significant role in consumer purchase decisions on e-commerce platforms. To address information overload in the context of online reviews, factors that drive review helpfulness have received considerable attention from scholars and practitioners. The purpose of this study is to explore the differential effects of discrete emotions (anger, disgust, fear, joy, sadness, and surprise) on perceived review helpfulness, drawing on cognitive appraisal theory of emotion and expectation-confirmation theory. Emotions embedded in 56,157 hotel reviews collected from TripAdvisor.com were extracted based on a transfer learning model to measure emotion variables as an alternative to dictionary-based methods adopted in previous research. We found that anger and fear have positive impacts on review helpfulness, while disgust and joy exert negative impacts. Moreover, hotel star-classification significantly moderates the relationships between several emotions (disgust, fear, and joy) and perceived review helpfulness. Our results extend the understanding of review assessment and have managerial implications for hotel managers and e-commerce vendors.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.8-17
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• 2019

The main purpose of this study isto explore the potential of affective computing (AC) platforms in education through two phases ofresearch: Phase I - platform analysis and Phase II - classification of academic emotions. In Phase I, the results indicate that the existing affective analysis platforms can be largely classified into four types according to the emotion detecting methods: (a) facial expression-based platforms, (b) biometric-based platforms, (c) text/verbal tone-based platforms, and (c) mixed methods platforms. In Phase II, we conducted an in-depth analysis of the emotional experience that a learner encounters in online video-based learning in order to establish the basis for a new classification system of online learner's emotions. Overall, positive emotions were shown more frequently and longer than negative emotions. We categorized positive emotions into three groups based on the facial expression data: (a) confidence; (b) excitement, enjoyment, and pleasure; and (c) aspiration, enthusiasm, and expectation. The same method was used to categorize negative emotions into four groups: (a) fear and anxiety, (b) embarrassment and shame, (c) frustration and alienation, and (d) boredom. Drawn from the results, we proposed a new classification scheme that can be used to measure and analyze how learners in online learning environments experience various positive and negative emotions with the indicators of facial expressions.

• Journal of Korea Entertainment Industry Association
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• v.14 no.8
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• pp.467-476
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• 2020

The purpose of this study is as a base study for improving the quality of online classes through multilateral analysis that examines the learning outcoms of educational quality and learning emotions on non-face-to-face online classes at Technical Colleges To this study, from March 1, 2020 to August 31, 2020, a survey was conducted on 1,000 students of H Technical Colleges located in the metropolitan area. The collected data were statistically processed using the SPSS Statistics 18.0 program, t-validation were performed to reveal awareness of online class also correlation analysis and multiple regression analysis were performed to reveal the relation and influence of factors related to quality of instruction, learning emotions, learning outcomes. First, there was a statistically significant difference in perception of online classes by gender and grade. Second, there was a positive correlation between the educational quality, learning emotions, and learning outcomes for online classes. Third, among the learning outcomes, the factors that influence the achievement were the educational content and positive emotions, and the factors that influence the satisfaction among the learning outcomes were the educational content and the learning environment.

• Journal of Gifted/Talented Education
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• v.25 no.1
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• pp.139-159
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• 2015

In this study, achievement emotions were compared between the gifted and regular students. The significant differences for each group were shown in all eight elements respectively in lesson situation, learning situation and test situation. Among various achievement emotions, it was also found that the gifted students showed higher level of positive achiecement emotions. Furthermore, positive achievement emotions can have positive effects in increasing the achievement level in science subject. On the other hand, the negative achievement emotions were higher for ordinary students that could have negative effets. The influential factors were recognizing the values of Science, Science experiments, positive evaluation experiences, aggressive learning attitudes, interests and knowledge, positive self-perceptions, career relationships, and teachers' recognitions. These factors influenced in imcreasing students' positive achievement emotions and decreasing negative achievement emotions. By conducting in-depth advanced conversations with students based on the above results could increase students' interest and positive achievement emotions.

• Journal of The Korean Association For Science Education
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• v.34 no.8
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• pp.745-754
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• 2014

Students experience a variety of achievement-related emotions during the process of learning the science curriculum. The purpose of this study is to develop an achievement emotions questionnaire for Korean middle school science curriculum to measure the achievement emotions that middle school students experience during study of this curriculum, and verified its validity. The Achievement Emotions Questionnaire-Korean Middle School Science is based on the English version of the Achievement Emotions Questionnaire, developed with reference to Korean middle school science curriculum and the characteristics of science study, from the perspective of the control-value theory of achievement. It has 232 questions, configured to measure nine achievement emotions across three types of academic settings. The questionnaire results can be treated with a high degree of confidence according to the result of our validation, which also verified that the achievement emotions of these students are configured with four internal criteria (learning strategy, achievement motivation and course grade), as suggested by the control-value theory; this in turn verifies that the nine achievement emotions are sufficiently distinctive across study situations. Last, it was verified that the questionnaire has sufficient external validity based on a comprehensive examination of the relation between science achievement emotions and the four criterion variables for each student. This suggests that through the development and implementation of this quantitative questionnaire, basic ground was provided to understand the achievement emotions experienced by middle school students learning the science curriculum.