Sentiment Analysis of University Libraries during the Covid-19 Pandemic in Indonesia

Abdurrahman Rahim Thaha

doi:10.35877/454RI.daengku1215

Authors

Abdurrahman Rahim Thaha Universitas Terbuka

DOI:

https://doi.org/10.35877/454RI.daengku1215

Keywords:

sentiment analysis, university library, review, Indonesia

Abstract

The purpose of this study was to analyze the sentiments of university library visitors during the pandemic. Data collection techniques using web scraping techniques using a data scraper application. The data is taken from Google reviews as many as 261 reviews from ten universities in Indonesia. Data analysis technique using Vader method for sentiment analysis and Ekman method for classification of emotional sentiment. The results of the sentiment analysis in this study show that visitor satisfaction is quite high at the university library during the pandemic. Positive sentiment in the university library is 80.8% and the classification of emotional sentiment is dominated by joy as much as 71.6% compared to other sentiments such as negative, fear, and sadness. These results indicate that visits to the university library can still be carried out well even though there are many limitations in activities in the library because of the pandemic.

References

Bhati, Pankaj, and Inder Kumar. 2020. “Role of Library Professionals in a Pandemic Situation like COVID-19.” International Journal of Library and Information Studies 10, no. 2: 33–48.
Brunner, Thomas A, Markus Stöcklin, and Klaus Opwis. 2008. “Satisfaction, Image and Loyalty: New versus Experienced Customers.” European Journal of Marketing.
Calheiros, Ana Catarina dos Santos. 2015. “Sentiment Analysis in Hospitality Using Text Mining: The Case of a Portuguese Eco-Hotel.”
Colneri?, Niko, and Janez Demsar. 2018. “Emotion Recognition on Twitter: Comparative Study and Training a Unison Model.” IEEE Transactions on Affective Computing.
Demšar, Janez, Tomaž Curk, Aleš Erjavec, ?rt Gorup, Tomaž Ho?evar, Mitar Milutinovi?, Martin Možina, Matija Polajnar, Marko Toplak, and Anže Stari?. 2013. “Orange: Data Mining Toolbox in Python.” The Journal of Machine Learning Research 14, no. 1: 2349–53.
Ekman, Paul, Wallace V Friesen, and Phoebe Ellsworth. 1972. Emotion in the Human Face: Guide-Lines for Research and an Integration of Findings: Guidelines for Research and an Integration of Findings. Pergamon.
Flemming, Christina. 2012. “A Joint University Library: Vision and Reality.” Library Management.
Helgesen, Øyvind, and Erik Nesset. 2011. “Does LibQUAL+ TM Account for Student Loyalty to a University College Library?” Quality Assurance in Education.
Kiran, K, and Singh Diljit. 2017. “Antecedents of Customer Loyalty: Does Service Quality Suffice?” Malaysian Journal of Library & Information Science 16, no. 2: 95–113.
Liu, Bing. 2015. Opinions, Sentiment, and Emotion in Text. Cambridge University Press.
Makhabel, B., Mishra, P., Danneman, N., & Heimann, R. 2017. R: Mining Spatial, Text, Web, and Social Media Data. Birmingham: Packt Publishing Ltd.
Murnawan, Murnawan. 2017. “Pemanfaatan Analisis Sentimen Untuk Pemeringkatan Popularitas Tujuan Wisata.” Jurnal Penelitian Pos Dan Informatika 7, no. 2: 109.
Pang, Bo, and Lillian Lee. 2008. “Opinion Mining and Sentiment Analysis.” Foundations and Trends® in Information Retrieval 2, no. 1–2: 1–135.
Thaha, Abdurrahman Rahim, and Fauzan Aziz. 2020. “Penambangan Teks Pada Tujuan Wisata Di Bandung Raya (Studi Kasus: Tangkuban Perahu Dan Kawah Putih).” Jurnal Sekretaris Dan Administrasi Bisnis 4, no. 2: 146–56.
Thomas, Emily H, and Nora Galambos. 2004. “What Satisfies Students? Mining Student-Opinion Data with Regression and Decision Tree Analysis.” Research in Higher Education 45, no. 3: 251–69.
Walsh, Benjamin, and Harjinder Rana. 2020. “Continuity of Academic Library Services during the Pandemic the University of Toronto Libraries’ Response.” Journal of Scholarly Publishing 51, no. 4: 237–45.