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Rochmat Husaini
Universitas Pembangunan Nasional "Veteran" Yogyakarta
Computer Science & IT Engineering

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Analisis Sentimen dan Emosi Vaksin Sinovac pada Twitter menggunakan Naïve Bayes dan Valence Shifter Bagus Muhammad Akbar; Ahmad Taufiq Akbar; Rochmat Husaini
Jurnal Teknologi Terpadu Vol. 7 No. 2: December, 2021
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v7i2.433

Abstract

The Sinovac vaccine is among the Covid-19 news in the world in early 2021. That information has led to public responses between the pros and cons. Through Twitter media, the public responds to the issue of the Sinovac; therefore, their opinions on Twitter can analyze to count the percentage of sentiment and emotion towards the Sinovac. This analysis hopes to provide a wise and objective reference, although the pros and cons information is still emerging. This study uses Rstudio for sentiment analysis through Twitter opinion classification using Naïve Bayes and the Valence Shifter Lexicon method to analyze emotions, also using the Naïve Bayes. The Data is 2000 English-language Twitter comments limited to the latest and most popular tweet based on the Sinovac keyword since February 1, 2021, from all Twitter users worldwide. The results showed that Naïve Bayes recognized 1433 (71.65%) positive sentiments, 403 (20.15%) negative sentiments, and 164 (8.2%) neutral sentiments. Meanwhile, Valence Shifter Lexicon recognized 903 (45.15%) positive sentiment, 437 (21.85%) negative sentiment, and 660 (33%) neutral sentiments. The Naïve Bayes also succeeded in recognizing emotions with the highest number 1727 (86.35%) mixed emotions and 141 (7.05%) joy emotion.
Co-Authors Ahmad Taufiq Akbar Akbar, Bagus Muhammad