Publication: Topic modeling and sentiment analysis on online learning experience during the pandemic
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Date
2023-04
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Don Mariano Marcos Memorial State University - South La Union Campus
Abstract
In this study, the researchers assess students' opinions towards online learning
through sentiment analysis.
To achieve this, the Support Vector Machine technique was applied to identify the
hyperplane that separates students' sentiments into positive and negative classes. The
user's thoughts and experiences with online learning were obtained, preprocessed,
sentimentally assessed, and then interpreted. In addition, topic modeling was performed
using the K-Means clustering algorithm to provide a cost-effective and efficient approach
to sentiment analysis. The specific objectives of this study are to determine the preprocessing techniques, train the sentiment analysis model, evaluate the model's
performance in terms of accuracy, precision, recall, and F J -Score, and to perform topic
model analysis. Thefindings from this study will provide useful information for academic
institutions and educators to improve their online learning strategies.
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Citation
Ugay, H. B., Canlas, M. V., Laceste, D. A. R., & Mendigoria, J. C. A. (2023) Topic modeling and sentiment analysis on online learning experience during the pandemic [Unpublished Undergraduate thesis]. Don Mariano Marcos Memorial State University - South La Union Campus, Agoo, La Union.