A comparative study of classification algorithms for sentiment analysis of COVID-19 vaccine opinions using machine learning
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Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The task of analyzing textual data and classifying them into positive, negative, or neutral
emotions within the domain of natural language processing is a multifaceted undertaking.
The primary objective of this study is to employ machine learning algorithms in order to
classify opinions pertaining to the coronavirus disease and vaccines. In this study, four
algorithms were employed, namely Random Forest (RF), Gradient Boosting Classifier
(GBC), Logistic Regression (LR), and Decision Tree (DT). The RF and GBC algorithms
demonstrated a commendable accuracy rate of 89%, while the LR and DT algorithms yielded
a slightly lower accuracy rate of 87%. The findings derived from this research can provide
valuable guidance to policymakers in effectively addressing potential barriers that may
impede the successful execution of vaccination campaigns. The analysis of the Kaggle data,
which encompasses a wide range of commentaries related to the pandemic and vaccines,
underscores the urgent need for prompt measures to attain herd immunity against Covid-19.
This imperative objective holds significant importance in effectively managing the
transmission of the virus and mitigating its adverse consequences on the well-being of the
general population. The task at hand necessitates the acknowledgment and resolution of
public apprehensions, as well as the establishment of trust and assurance in the vaccination
initiative. This study presents an analysis of the machine learning techniques employed and
conducts a comparative evaluation of their significance. The forthcoming research endeavors
to create an application that will be capable of categorizing sentiments and opinions
pertaining to diseases and vaccines. It is imperative for governments and organizations to comprehend the obstacles linked to the worldwide COVID-19 vaccination endeavor in order
to develop efficacious strategies. Nevertheless, it is crucial to acknowledge that the scope of
the analysis was restricted to tweets written in the English language. This limitation may
potentially undermine the credibility and generalizability of the findings pertaining to overall
sentiment. Additional investigation could be conducted to examine more extensive Twitter
datasets employing deep learning models in order to gain a deeper comprehension of the
general public's attitudes towards COVID-19 vaccines.
Açıklama
Anahtar Kelimeler
Twitter Sentiment Analysis, NLP, RF, DT, LR, GBC, Covid-19 Vaccines
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Zainulabdeen, D. S. Z. (2024). A comparative study of classification algorithms for sentiment analysis of COVID-19 vaccine opinions using machine learning. (Yayınlanmamış yüksek lisans tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul.
