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5. THINK ABOUT WHAT YOU'VE LEARNED: ASPECT-BASED SENTIMENT ANALYSIS FOR MODELING USER EXPERIENCE IN THE FIELD OF ONLINE EDUCATION (OPEN ACCESS)

THINK ABOUT WHAT YOU'VE LEARNED: ASPECT-BASED SENTIMENT ANALYSIS FOR MODELING USER EXPERIENCE IN THE FIELD OF ONLINE EDUCATION (OPEN ACCESS)

Kirina Margarita Aleksandrovna

Junior Research Fellow of Linguistic Convergence Laboratory, Lecturer of Department of Philology, National Research University Higher School of Economics, St. Petersburg, Russia, mkirina@hse.ru

Abstract

The article focuses on the application of opinion mining techniques to evaluate user experience on the Hyperskill educational platform, using Python, Java, and Kotlin programming projects as the basis of analysis. The study utilizes sentiment analysis and keyword extraction methods to gauge users' attitudes towards the platform, learning process, and topics covered. To achieve this, the VADER and RAKE-NLTK algorithms are employed to determine polarity and extract aspect terms respectively. The findings demonstrate that the combination of these tools is highly effective for conducting an aspect-based analysis of students' feedback.

Keywords

computational linguistics; natural language processing; sentiment analysis; keywords extraction; user experience evaluation; online education; student feedback

For citing: Kirina M.A. (2024). THINK ABOUT WHAT YOU'VE LEARNED: ASPECT-BASED SENTIMENT ANALYSIS FOR MODELING USER EXPERIENCE IN THE FIELD OF ONLINE EDUCATION. Human being: Image and essence. Humanitarian aspects. Moscow: INION RAN. Vol. 2(58). Р. 176-204. DOI: 10.31249/chel/2024.02.10

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