Sentiment Analysis of Google Classroom Application Using Support Vector Machine

Technological advances are growing very rapidly and affect human life and also have an impact during the pandemic. During the pandemic, Google Classroom ranks first on the platform that is often used during distance learning. On the Google Play site, many reviews about the application. Due to the ma...

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Bibliographic Details
Published in2022 6th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM) pp. 117 - 120
Main Authors Panjaitan, Nismah, Manurung, Natasya
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.11.2022
Subjects
Communications technology
Computer aided instruction
Covid-19
Data visualization
Google Classroom
Pandemics
Programming
Python Programming Language
Sentiment analysis
Support Vector Machine
Support vector machines
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Summary:Technological advances are growing very rapidly and affect human life and also have an impact during the pandemic. During the pandemic, Google Classroom ranks first on the platform that is often used during distance learning. On the Google Play site, many reviews about the application. Due to the many reviews given by users, sentiment analysis is carried out so that public opinion on the application is known, thus users or application developers know the advantages and disadvantages of the application. Sentiment analysis was carried out using the SVM algorithm with the help of the python programming language on google colab. From the results of sentiment analysis, the sentiment of Google Classroom application users is 64% negative sentiment and 36% positive sentiment with an algorithm accuracy of 81%. In addition, data visualization is carried out, so that it can be seen what causes users to give positive reviews and negative reviews, from which priority improvements are obtained to improve Google Classroom performance.
DOI:10.1109/ELTICOM57747.2022.10038262