Emotion Detection based on Column Comments in Material of Online Learning using Artificial Intelligence

Wahyono, Irawan Dwi; Saryono, Djoko; Putranto, Hari; Asfani, Khoirudin; Rosyid, Harits Ar; Sunarti,; Mohamad, Mohd Murtadha; Said, Mohd Ismid Mohd; Horng, Gwo-Jiun; Shih, Jhih Yuan

doi:10.3991/ijim.v16i03.28963

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…The study developed a utility cellular for emotion detection from column comments in online media. Accuracy of mobile application utility is 70% for emotion detection [23]. Humans are easily prone to errors in interpreting text-based emotions.…”

Section: Literature Reviewmentioning

confidence: 99%

Multifaceted Sentiment Detection System (MSDS) to Avoid Dropout in Virtual Learning Environment using Multi-class Classifiers

T¹,

J²

2023

IJACSA

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Sentiment analysis with machine learning plays a vital role in Higher Educational Institutions (HEI) for decision making. Technology-enabled interactions can only be successful when a strong student-teacher link is established, and the emotions of students are clearly comprehended. The paper aims at proposing Multifaceted Sentiment Detection System (MSDS) for detecting sentiments of higher education students participating in virtual learning and to classify the comments posted by them using Machine Learning (ML) algorithms. Present research evaluated a total of n=1590 students' comments with the presence of three specific multifaceted characteristics each providing 530 comments to perform Sentiment Analysis (SA) for monitoring their sentiments, opinions that facilitate predicting dropout in virtual learning environment (VLE). This begins with the phrase extraction; then data pre-processing techniques namely digits, punctuation marks and stop-words removal, spelling correction, tokenization, lemmatization, ngrams, and POS (Part of Speech) are applied. Texts are vectorized using two feature extraction techniques with count vectorization and TF-IDF metrics and classified with four multiclass supervised ML techniques namely Random Forest, Linear SVC, Multinomial Naive Bayes, and Logistic Regression for multifaceted sentiment classification. Analyzing students' feedback using sentiment analysis techniques classifies their positive, negative, or even more refined emotions that enables dropout prediction. Experimental results reveal that the highest mean accuracy result for device efficiency, cognitive behavior, technological expertise with cloud learning platform usage were achieved by Logistic Regression with 98.49%, Linear SVC with 93.58% and Linear SVC with 92.08% respectively. Practically, results confirm feasibility for detecting students' multifaceted behavioral patterns and risk of dropout in VLE.

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Section: Literature Reviewmentioning

confidence: 99%

Multifaceted Sentiment Detection System (MSDS) to Avoid Dropout in Virtual Learning Environment using Multi-class Classifiers

T¹,

J²

2023

IJACSA

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“…Text mining is a form of artificial intelligence that is essential to the internet or internet learning and is increasingly used in mobile apps. To assist educators or video producers in raising the calibre of the videos that are produced based on feedback in the form of user emotion, AI must be integrated into the web or mobile platforms [1].…”

Section: Introductionmentioning

confidence: 99%

Visualization for Emotion Detection in Mobile-Based Land Monitoring Using User-Centred Design

Victoria

Supraja²,

Gayathri³

et al. 2023

Int. J. Interact. Mob. Technol.

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Many colleges employ online learning as media learning where each piece of multimedia, be it sound, film, or text, can receive feedback from students. The lecture wants to know about the emotions that students experienced when they accessed the media, such as happiness, disappointment, or unhappiness, and instructors want to know how joyful they felt. This study built a tool cell for online media to use in identifying emotions in column comments. This paper focuses on user experience (UX) design, which is based on the Human-Centred Design method that prioritizes empathy for users and focuses on user demands to meet the discovery of user wants and develop high-fidelity prototypes. The results of the testing show that the average accuracy is 1.68%, the average recall is 1.55, the average precision is 1.45, and the average accuracy for the use of this phone website is 80% for emotion recognition based only on a column of comment in the internet media.

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YouTube Sentimental Analysis Using a Combined Approach of KNN and K-means Clustering Algorithm

Adhikari,

Kaushik,

Obaid

et al. 2023

Advances in Intelligent Systems and Computing

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Emotion Detection based on Column Comments in Material of Online Learning using Artificial Intelligence

Cited by 8 publications

References 18 publications

Multifaceted Sentiment Detection System (MSDS) to Avoid Dropout in Virtual Learning Environment using Multi-class Classifiers

Multifaceted Sentiment Detection System (MSDS) to Avoid Dropout in Virtual Learning Environment using Multi-class Classifiers

Visualization for Emotion Detection in Mobile-Based Land Monitoring Using User-Centred Design

YouTube Sentimental Analysis Using a Combined Approach of KNN and K-means Clustering Algorithm

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