Indonesian Twitter Sentiment Analysis Application on The Covid l9 Vaccine Using Naive Bayes Classifier

Erfina, Adhitia; Ndk, Moneyta Dholah Rosita; Hidayat, Rahmat; Subagja, Aris; Ramadhan, Haerul; Lesmana, Teddy; Saepudin, Sudin; Muslih, Muhamad

doi:10.1109/icced53389.2021.9664864

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…The difference is mainly caused by the multifaceted approach in studying a global phenomenon that has affected people and economies differently worldwide. As a result, the sentiment analysis studies have been conducted at the country-related level for better extracting people's opinions related to the various measures governments were taking for limiting or stopping the spread of the pandemic [14][15][16][17]. Also, as the pandemic has affected people with different health conditions and of different ages, the sentiment dynamics related to the government decisions has boosted the discourse on various social media platforms, enhancing the body of literature related to how the sentiments fluctuate as a response to major events or policy changes [18].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis in the Age of COVID-19: A Bibliometric Perspective

Sandu,

Cotfas,

Delcea

et al. 2023

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The global impact of the COVID-19 pandemic has been profound, placing significant challenges upon healthcare systems and the world economy. The pervasive presence of illness, uncertainty, and fear has markedly diminished overall life satisfaction. Consequently, sentiment analysis has gained substantial traction among scholars seeking to unravel the emotional and attitudinal dimensions of this crisis. This research endeavors to provide a bibliometric perspective, shedding light on the principal contributors to this emerging field. It seeks to spotlight the academic institutions associated with this research domain, along with identifying the most influential publications in terms of both paper volume and h-index metrics. To this end, we have meticulously curated a dataset comprising 646 papers sourced from the ISI Web of Science database, all centering on the theme of sentiment analysis during the COVID-19 pandemic. Our findings underscore a burgeoning interest exhibited by the academic community in this particular domain, evident in an astonishing annual growth rate of 153.49%. Furthermore, our analysis elucidates key keywords and collaborative networks within the authorship, offering valuable insights into the global proliferation of this thematic pursuit. In addition to this, our analysis encompasses an n-gram investigation across keywords, abstracts, titles, and keyword plus, complemented by an examination of the most frequently cited works. The results gleaned from these endeavors offer crucial perspectives, contribute to the identification of pertinent issues, and provide guidance for informed decision-making.

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Section: Introductionmentioning

confidence: 99%

Sentiment Analysis in the Age of COVID-19: A Bibliometric Perspective

Sandu,

Cotfas,

Delcea

et al. 2023

Information

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“…Dengan menggunakan algoritma Naïve Bayes Classifier data tersebut diproses dan menghasilkan nilai akurasi tertinggi untuk covid vaccine sebesar 94,74% dengan polaritas sentimen tertinggi yaitu sentimen positif. Adapun jenis vaksin yang mendapatkan polaritas sentimen positif tertinggi yaitu Sinopharm Vaccine dan yang mendapatkan polaritas sentimen negarif tertinggi yaitu astrazeneca vaccine [12].…”

Section: Tinjauan Literaturunclassified

Sentiment Analysis of Electric Vehicles using the Naïve Bayes Algorithm

Erfina

Lestari²

2023

SISTEMASI

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Resolusi Industri yang semakin berkembang membuat banyak transisi disegala bidang. Penggunaan Kendaraan BBM yang meningkat membuat stok bahan bakar minyak Indonesia menipis. Kendaraan listrik merupakan salah satu jenis kendaraan yang menggunakan tenaga listrik. Dengan hadirnya kendaraan listrik diharapkan menjadi solusi atas pernghematan bahan bakar minyak. Kelebihan lain dari kendaraan listrik menyebutkan bahwa kendaraan listrik menghasilkan emisi yang rendah sehingga selain menghemat stok bahan bakar minyak juga pencemaran udara akan semakin berkurang. Namun demikian kehadiran kendaraan listrik masih menjadi pro dan kontra di masyarakat. Salah satu media yang yang menjadi tempat masyarakat mengeluarkan pendapat adalah media sosial Youtube. Pengujian dilakukan dengan menggunakan K-Fold Cross Validation dan algoritma Naïve Bayes. Dari pengujian tersebut dihasilkan nilai akurasi sebesar 82% dengan polaritas sentimen yang didapat yaitu sentimen negatif sebesar 82% sedangkan sentimen positif sebesar 18%. Tujuan dari adanya penelitian ini adalah untuk mengetahui polaritas sentimen serta nilai akurasi yang didapatkan. Diharapkan dengan adanya penelitian ini dapat tersampaikan kepada para pihak terkait dengan baik guna kemajuan dimasa yang akan datang

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