Can e-Learner's emotion be recognized from interactive Chinese texts?

Tian, Feng; Zhao, Ruomeng; Chen, Tonghao; Jia, Xinyan

doi:10.1109/cscwd.2009.4968116

Cited by 13 publications

(4 citation statements)

References 9 publications

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“…The final section covers the conclusion and plans for future works. Tian et al (2009) developed an e-learner's affective category model that applies a questionnaire. They first extract an emotion feature from the corpus on the Internet.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Arabic Tweets in e-Learning

AL-Rubaiee

Qiu

Alomar

et al. 2016

Journal of Computer Science

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Abstract:In this study, we present the design and implementation of Arabic text classification in regard to university students' opinions through different algorithms such as Support Vector Machine (SVM) and Naive Bayes (NB). The aim of the study is to develop a framework to analyse Twitter "tweets" as having negative, positive or neutral sentiments in education or, in other words, to illustrate the relationship between the sentiments conveyed in Arabic tweets and the students' learning experiences at universities. Two experiments were carried out, one using negative and positive classes only and the other one with a neutral class. The results show that in Arabic, a sentiments SVM with an n-gram feature achieved higher accuracy than NB both with using negative and positive classes only and with the neutral class.

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Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Arabic Tweets in e-Learning

AL-Rubaiee

Qiu

Alomar

et al. 2016

Journal of Computer Science

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“…Modern AI-powered tools excel at discovering polarity, classifying, and predicting emotions within unlabeled sets of comments (23). Numerous studies have explored sentiment analysis (SA) in education, with a substantial focus on e-learning, classroom learning, and daily sessions and real-time interventions (23,24).…”

Section: Related Workmentioning

confidence: 99%

Towards an improved of teaching practice using Sentiment Analysis in Student Evaluation

Peña-Torres

2024

inycomp

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Student evaluation of teaching (SET) is an ad-hoc way of assessing teaching effectiveness in higher education institutions. In this paper, we present an approach to analyzing sentiments expressed in SET comments using a large language model (LLM). By employing natural language processing techniques, we extract and analyze sentiments expressed by students when the course has ended, aiming to provide educators and administrators with valuable insights into teaching quality and elements to improve teaching practice. Our study demonstrates the effectiveness of LLMs in sentiment analysis of comments, highlighting their potential to enhance the evaluation process. Our experiments with a crowdsourced tagged dataset show a 93% of accuracy in the classification of feedback messages. We discuss the implications of our findings for educational institutions and propose future directions for research in this domain.

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“…In general, feedback on motivation and self-esteem, related to the student's emotional state, is also encouraged in the e-learning, even when it concerns negative content such as, for example, bad students' performances (Hattie & Timperley, 2007). Tian, et al (2009) discuss an approach to identify e-learner emotion, creating patterns to find which words are associated more with emotions, abstracting, from corpus, Chinese metaphors employing sentence meaning and structure. Furthermore, the authors exploited a case based reasoning recommendation strategy to improve emotion communication between two interacting sides.…”

Section: Emotion and Sentiment Analysis: A Fruitful Interactionmentioning

confidence: 99%

Sentiment Analysis to Evaluate Teaching Performance

Lytras

D'Avanzo

Adinolfi

et al. 2016

International Journal of Knowledge Society Research

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Feedback Evaluation is a necessary part of any institute to maintain and monitor the academic quality of the system. Traditionally, a questionnaire based system is used to evaluate the performance of teachers of an institute. Here, we propose an automatic evaluation system based on sentiment analysis, which shall be more versatile and meaningful than existing system. In our proposed system, feedback is collected in the form of running text and sentiment analysis is performed to identify important aspects along with the orientations using supervised and semi supervised machine learning technique

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Can e-Learner's emotion be recognized from interactive Chinese texts?

Cited by 13 publications

References 9 publications

Sentiment Analysis of Arabic Tweets in e-Learning

Sentiment Analysis of Arabic Tweets in e-Learning

Towards an improved of teaching practice using Sentiment Analysis in Student Evaluation

Sentiment Analysis to Evaluate Teaching Performance

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