Emotion Analysis of Writers and Readers of J apanese Tweets on Vaccinations (2022)

Abstract: Public opinion in social media is increasingly becoming a critical factor in pandemic control. Understanding the emotions of a population towards vaccinations and COVID-19 may be valuable in convincing members to become vaccinated. We investigated the emotions of Japanese Twitter users towards Tweets related to COVID-19 vaccination. Using the WRIME dataset, which provides emotion ratings for Japanese Tweets sourced from writers (Tweet posters) and readers, we fine-tuned a BERT model to predict levels of emotional intensity. This model achieved a training accuracy of MSE = 0.356. A separate dataset of 20,254 Japanese Tweets containing COVID-19 vaccine-related keywords was also collected, on which the fine-tuned BERT was used to perform emotion analysis. Afterwards, a correlation analysis between the extracted emotions and a set of vaccination measures in Japan was conducted.The results revealed that surprise and fear were the most intense emotions predicted by the model for writers and readers, respectively, on the vaccine-related Tweet dataset. The correlation analysis also showed that vaccinations were weakly positively correlated with predicted levels of writer joy, writer/reader anticipation, and writer/reader trust.

## introduction vaccination against covid-19 has been demonstrated to reduce the spread of the virus @xcite @xcite . however, vaccine hesitancy can prevent vaccine uptake, increasing risk of infection. searching for and understanding causes of vaccine hesitancy can lead to more effective methodologies in convincing community members to become vaccinated. one possible area of understanding vaccine hesitancy is the emotions felt towards vaccines and the covid-19 pandemic. for example, fear felt towards vaccination might discourage one from receiving the vaccine. meanwhile, fear felt towards contracting covid-19 could encourage one to become vaccinated against it. leveraging emotions has also been proposed in communication to reduce covid-19 vaccine hesitancy @xcite . there have been several attempts at extracting emotions regarding covid-19 vaccines, particularly from social media. the wealth of information available on social networking services such as twitter already makes them a popular source for mining sentiment and emotions in other areas such as politics @xcite and consumerism @xcite . social media information extraction has seen continued research during the covid-19 pandemic, with multiple works specifically seeking to mine sentiments and emotions surrounding the pandemic and vaccination @xcite @xcite . existing emotion analysis studies on covid-19related japanese twitter corpora only focus on the emotions of writers, or those who post tweets @xcite . however, the emotions of readers, or those who read tweets, are not necessarily the same as those of writers. for instance, if a writer expresses disgust towards vaccination, a reader might express anger out of disagreement in response. these reader emotions may also contain useful information in understanding vaccine hesitancy. we contribute to this research area by using a bert to extract emotions of both writers and readers towards tweets related to covid-19 vaccines and comparing the predicted emotions to vaccination uptake. we do this by fine-tuning a bert @xcite to predict intensity scores for plutchik's eight emotions @xcite for writers and readers from tweets containing keywords related to covid-19 vaccination, and performing a correlation analysis between the mined emotions and a set of vaccination measures in japan. note that one limitation of our study is that the dataset of covid-19 vaccine-related tweets does not guarantee that covid-19 or vaccination are the topics of the texts, or are necessarily the object of any inferred emotion. we find that the emotion most prominently predicted by the model for writers on the vaccinerelated tweet dataset is surprise, with fear being the most intensely predicted emotion for readers. additionally, writer joy, writer/reader anticipation, and writer/reader trust are weakly positively correlated with vaccinations. ## related work prior to the adoption of deep learning, emotion analysis of tweets was performed with a combination of feature engineering, lexicon-based approach, and traditional off-the-shelf classifiers. @xcite and @xcite engineered overlapping sets of features from tweets, with shared features including n-grams, pos, adjectives, and lexicon-based sentiment polarity scores. @xcite fed the features to an svm while @xcite used linear and naive bayes classifiers. empatweet (2012) used a similar set of features and also an svm, but exchanged sentiment polarity scores for synonym rings, hypernyms, and lda topic scores. however, these classical methods have been outperformed by more contemporary and dedicated sequence modelling techniques such as rnns and lstms. @xcite demonstrated the superiority of lstms in emotion analysis over svms and naive bayes on thai twitter text, while @xcite showed the effectiveness of character-based rnns. the introduction of transformers @xcite as the new state-of-the-art sequence modelling architecture and their increasing ubiquity has also lead to their application in social media emotion analysis for a variety of languages. bert models can outperform cnns and bilstms on english twitter @xcite ; naive bayes, logistic regression, and svms on romanian twitter @xcite ; and tf-idf and word2vec on arabic twitter (al-twairesh, 2021). during the covid-19 pandemic, a roberta @xcite was fine-tuned on emotion analysis to classify the emotions of tweets containing hashtags related to the pandemic @xcite . meanwhile, emotion analyses of japanese tweets related to covid-19 have been conducted using traditional techniques such as lexicon-based methods (bashar, 2021) and frequency analysis @xcite . the work most similar to ours is that of @xcite , who perform sentiment analysis of covid-19-related japanese tweets; investigate the correlation of the mined sentiments with infections, deaths, and vaccinations; and conduct additional analyses comparing multiple vaccine brands. our study differs from this by extracting emotions rather than sentiments from both writers and readers, and comparing these mined features to vaccinations, vaccinated people, and fully vaccinated people. ## broader impact research into understanding the emotions of a population towards vaccines could hold both positive and negative societal impacts. any relationship discovered between emotions and vaccinations could be leveraged in campaigns aimed at convincing citizens to receive vaccinations, reducing the spread of covid-19. on the other hand, knowledge of what emotions could affect vaccine acceptance can be used in efforts to increase vaccination hesitancy and slow down vaccination rates, which could prolong risks of infection. ## conclusion we fine-tuned a bert on the task of emotion analysis, and used the emotion analysis bert to infer emotion intensities of writers and readers from a corpus of tweets containing keywords related to covid-19 and covid-19 vaccination. our results revealed that surprise and fear were respectively the most intensely predicted emotions for writers and readers. furthermore, vaccinations were weakly positively correlated with writer joy, writer anticipation, writer trust, reader anticipation, and reader trust. future works can extend this study by designing the emotion analysis to be aspect-based with respect to the keywords, as it is possible that the keywords are not the objects of the inferred emotions. another possible area for further research could be correlation analyses with other covid-19 metrics, such as infections and reproduction rate. (a) with vaccinations. (b) with people vaccinated. (c) with people fully vaccinated. unison model. ieee transactions on affective computing, 11(3):433-446. jacob devlin, ming-wei chang, kenton lee, and kristina toutanova. 2019. bert: pre-training of deep bidirectional transformers for language understanding. in proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pages 4171-4186, minneapolis, minnesota. association for computational linguistics.