Sentiment Analysis of the Impact of the Covid-19 Pandemic on Students and College Students through Twitter Social Media
DOI:
https://doi.org/10.46961/mediasi.v3i3.585Keywords:
Sentiment Analysis, Pandemic Effect, Twitter, Scraping, Text Mining,Abstract
The Covid-19 pandemic has had an impact on adjusting the implementation of economic and social activities as well as education by prioritizing public safety and health, including students. The purpose of this research was to obtain the results of sentiment analysis on the impact of the pandemic on students by extracting opinions in the form of text on Twitter social media. The benefit of the results of the sentiment analysis is that solutions can be found for those affected by the pandemic. The method used in sentiment analysis consists of 5 stages, namely data collection, text preparation, sentiment detection, sentiment classification, and output analysis. The research results stated that negative sentiment was the largest sentiment, namely 88%, followed by positive sentiment as much as 6% and neutral sentiment as much as 5.3%.
References
Alessia, D. et al. (2015). Approaches, tools and applications for sentiment analysis implementation. International Journal of Computer Applications, 2015, 125.3.
Bonta, V., & Janardhan, N. K. N. (2019). A Comprehensive Study on Lexicon-Based Approaches for Sentiment Analysis. Asian Journal of Computer Science and Technology, 8(S2), 1-6.
Ghebreyesus, T.A. (2020). WHO Director-General's opening remarks at the media briefing on COVID-19 – 11 March 2020". www.who.int (dalam bahasa Inggris). Diakses tanggal 2020-03-22. Diakses pada 16 Januari 2020 pukul 15.30 WIB.
Kurniawan, R., & Apriliani, A. (2020). Analisis Sentimen Masyarakat tterhadap Virus Corona Berdasarkan Opini dari Twitter Berbasis Web Scraper. Jurnal INSTEK (Informatika Sains dan Teknologi), 5(1), 67-75.
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113.
Mukherjee, S. (2021). Sentiment Analysis. In: ML.NET Revealed. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-6543-7_7
Putri, T.A.W. (2022). Analisis Sentimen Dampak Pandemi Covid-19 Terhadap Kesehatan Mental Menggunakan Klasifikasi Support Vector Machine (SVM). https://repository.unej.ac.id/handle/123456789/106283. Universitas Jember.
Tim Penyusun. (2016). KBBI Daring dengan basis Kamus Besar Bahasa Indonesia Edisi Kelima. https://kbbi.kemdikbud.go.id/entri/sentimen. Diakses 4 Agustus 2022.
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