Sentiment analysis on feedback of higher education teaching conduct: An empirical evaluation of methods

Jimmy, Jimmy and Prasetyo, Vincentius Riandaru (2022) Sentiment analysis on feedback of higher education teaching conduct: An empirical evaluation of methods. In: INTERNATIONAL CONFERENCE ON INFORMATICS, TECHNOLOGY, AND ENGINEERING 2021 (InCITE 2021): Leveraging Smart Engineering, 25 - 26 August 2021, Surabaya, Indonesia.

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Sentiment analysis aims to automatically identify and classify the tone or polarity of people’s opinion in an unstructured text. This paper focuses on evaluating the accuracy of sentiment analysis methods to classify Indonesian higher education teaching conduct. In this context, we make the following contributions: (1) evaluation on the impact of text preparation methods in term of accuracy of sentiment analysis results, (2) evaluation the accuracy of three popular sentiment analysis methods (i.e., Naive Bayes, Support Vector Machine, Decision Tree) in classifying Indonesian text, and (3) proposal and evaluation on the effectiveness of combining the results from the three methods considered in this study with hope to improve the results’ accuracy. Finally we analyzed cases where all classifiers suggested incorrect sentiment classification and highlighted areas for future works to improve the accuracy of sentiment analysis, in particular for Indonesian Text.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Sentiment Analysis, Feedback, High Education Teaching Conduct
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Engineering > Department of Information Technology
Depositing User: JIMMY 61156 206002
Date Deposited: 26 Apr 2022 05:04
Last Modified: 26 Apr 2022 06:57

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