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Students’ Sentiment and Feedback Analysis on Online Learning System during COVID-19 Pandemic


After the COVID-19 pandemic, no one refutes the importance of online learning in the educational process. Measuring student engagement is a crucial step towards online learning. 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, sentiment analysis is growing yet remains challenging. An online learning system can automatically adapt to learners’ emotions and provide feedback about their motivations. The researchers’ challenge is how to know the student behaviour by feedback text. Most existing techniques of sentiment analysis focus only on the abstract level, broadly classifying sentiments into positive, neutral, or negative, and lacking the ability to perform fine-grained sentiment analysis. This study proposes a supervised ML-based sentiment analysis model that includes two main machine learning models. The first one is sentiment detection. There is two distinct (0 and 1) label as positive and negative. And the second one is emotional detection. There are six mains (1-6) labelled as Anger, Joy, Surprise, Disgust, Sadness, Fear. Most of the current ML-based Sentiment Analysis models used Support Vector Machine, Naive Bayes algorithms, Logistic Regression and experiment with other algorithms such as Decision Tree and Random Forest also

2022 Papers

Local and Global Orientation Correction for Oriented Human (Pose) Detection

Preliminary Study on SSCF-derived Polar Coordinate for ASR

Text Recognition on the Khmer Identification Cards and Its Application in Electronic Know Your Customer (e-KYC)

Exploration of Semantic Information of Previous Sentences for Automatic Speech Recognition

Cambodia Distributed Ledger – CamDL

Job Trends Analysis Using Power BI

Temperature Forcasting in Pnhom Penh Using Time Series Models

Eveluation of Regularization based Contiual Learning Alogorithm in the Context of Human Activity Recognition

Implementation of Deep Learning for Smart City Application: Lessons Learned

Intelligent Control in SDN/NFV-Empowered IoT System for Smart City Application

ENI-ETSI Meets the Proactive Network Solutions for Multi-tier Networking


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