Sentiment Analysis of COVID-19 Vaccine on Twitter Social Media: Systematic Literature Review

This study attempted to review studies on sentiment analysis of COVID-19 vaccine using Twitter as the source data. It was conducted to understand methods to collect, preprocess and classify data by researchers. We used Systematic Literature Review approach to collect, filter and review research papers found. Research papers were collected from IEEE, ScienceDirect, Springer, ACM Digital Library, arXiv, medRxiv and Google Scholar by taking studies published from 2020 until June 2021. First of all, we gathered paper based on title and it resulted forty papers. On the next step, the contents of the papers were examined using exclusion criteria and inclusion criteria to investigate them further. This step filtered out several papers leaving twenty one corresponding papers to be reviewed for this study. We reviewed the remaining papers to answer four predetermined research questions. The frequently used methods to collect data are RTweet, Twint, Twitter API and Tweepy. Several techniques which used by researchers to preprocess Twitter data are stop word removal, remove punctuation and link, case folding, tokenization, stemming, remove duplicate tweet and lemmatization. To classify sentiment tweets, researchers used several machine learning and deep learning methods. BERT as Transformers method was also used by several researchers. Further studies regarding methods and variables or parameters to classify tweet data are still needed.