Predicting the sentiment of South Korean Twitter users toward vaccination after the emergence of COVID-19 Omicron variant using deep learning-based natural language processing

Abstract

Although the full vaccination rate of South Korea compared to other countries, concerns about the effectiveness of the vaccine are growing as new COVID variants such as Alpha, Beta, Gamma, Delta, and Omicron appear over time. In this study, we collected Twitter data in South Korea that contained keywords like vaccines after the outbreak of the Omicron variant from 27 November 2021 to 14 February 2022. First, we analyzed the relationship between potential keywords associated with vaccination after the appearance of the Omicron variant in Twitter using network analysis. Second, we developed an efficient model for predicting the emotion of speech regarding vaccination after the COVID-19 Omicron variant pandemic by using deep learning algorithms. We constructed sentiment analysis models regarding vaccination after the COVID-19 Omicron pandemic by using five algorithms [i.e., support vector machine (SVM), recurrent neural networks (RNNs), long short-term memory models (LSTMs), bidirectional encoder representations from transformers (BERT), and Korean BERT (KoBERT)]. The results confirmed that KoBERT showed the best performance (71%) in all predictive performance indicators (accuracy, precision, and F1 score). It is necessary to prepare measures to alleviate the negative factorss of the public about vaccination in the long-term pandemic situation and help the public recognize the efficacy and safety of vaccination by using big data based on the results of this study.

Analysis of green deal communication on twitter: environmental and political perspective

“…Sentiment analysis was used to better understand the social atmosphere related to the Green Deal. This method analyzes people's emotions, attitudes, evaluations, and opinions communicated through unfiltered social network posts (Elbagir and Yang, 2019;Eom et al, 2022).…”

Year 2022 in Medical Natural Language Processing: Availability of Language Models as a Step in the Democratization of NLP in the Biomedical Area

“…Nevertheless, the trends observed within this set of keywords are also reflected in the analysis provided in the following sections. [23], construction of cohorts of similar patients [24], processing of electronic medical records [25], understanding of patient's answers in a French medical chatbot [26]; • German: evaluation of Transformers on clinical notes [27]; • Greek: improving the performance of localized healthcare virtual assistants [28]; • Hindi: classification of COVID-19 texts [29], chatbot for information sexual and reproductive health for young people [30]; • Italian: analysis of social media for quality of life in Parkinson's patients [31], sentiment analysis of opinion on COVID-19 vaccines [32,33], estimation of the incidence of infectious disease cases [34]; • Japanese: understanding psychiatric illness [35], detection of adverse events from narrative clinical documents [36]; • Korean: BERT model for processing med-ical documents [37], sentiment analysis of tweets about COVID-19 vaccines [38];…”

“…and institutions (like MIMIC-III), as well as data from social media, hospitals, bibliographical datasets, clinical trials, etc. The research in other languages is possible mainly thanks to the availability of data from social media [7,9,19,20,22,38,43,47] and documents from local hospitals [10,13,14,17,18,23,25,27,36,37,40,42]. Besides, this set of works in languages other than English relies on the dedicated language models, which cover a great variety of languages by now.…”

“…• when other sources of information are not freely available, such as in languages other than English [7,9,19,20,22,38,43,47]; • when researchers investigate questions related to patients and population, while these questions are not discussed with medical doctors or require large population samples. We can mention for instance sentiment analysis on medication and vaccines [7,20,33,38,43,47], and adverse drug effects [76,77]; • when mental health of patients is concerned in cases like depression [9, 4, 78], eating disorders [79], suicide detection and prevention [19,80], quality of life of patients [31,81], and drug misuse [82][83][84].…”

Detecting the most critical clinical variables of COVID-19 breakthrough infection in vaccinated persons using machine learning

“…A breakthrough infection is present when someone who completed vaccination still gets infected. 3 Lately, the use of Artificial Intelligence (AI) methods for tackling the challenges around diagnosing and treating COVID-19 has received significant attention. Some of the efforts so far reported include the application of machine learning (ML) to predict or detect SARS-CoV-2 breakthrough infections.…”