Analysis of International Public Emotional Responses
Toward the COVID-19 Vaccine
Satoshi Fukuda
Faculty of Science and Engineering
Chuo University
Tokyo, Japan
fukuda.satoshi.3238@kc.chuo-u.ac.jp

Hidetsugu Nanba
Faculty of Science and Engineering
Chuo University
Tokyo, Japannanba@kc.chuo-u.ac.jp

Abstract— The pharmaceutical companies developing the
COVID-19 vaccine and national governments continuously
disseminate information regarding the vaccine’s effectiveness andvaccination status to reassure the public about vaccination.
However, because interest in vaccines and vaccination status vary
between the populations of different countries, it is likely thatemotional responses toward vaccines also vary between countries.
The current study analyzed and compared the emotions expressedby people in Japan, the United States, Great Britain, Canada,
Australia, and India regarding the vaccine by analyzing tweets, on
the basis of the Plutchik's wheel of emotions. In addition, a textual
analysis was conducted using dependency analysis and burst
detection to obtain information regarding positive changes inpeople’s feelings toward vaccines. Our approach was useful forclarifying the emotional responses of people in various countries
regarding the vaccine, and informing approaches for positively
changing people’s feelings about vaccines.
Keywords—Twitter, Sentiment Analysis, COVID-19 Vaccine

I. INTRODUCTION
As of 2022, the outbreak of novel coronavirus disease 2019
(COVID-19) is still spreading around the world, and continues
to have a significant impact on people’s lives. To date, various
policies have been implemented to control the spread of
COVID-19, including mask-wearing recommendations andlockdowns. Among these policies, vaccination has received alarge amount of public interest internationally, and vaccine
development by pharmaceutical companies and promotion of
vaccination by governments are underway. Pharmaceuticalcompanies and national governments are constantly
disseminating information such as the effect of the vaccine andvaccination status to reassure people. However, public
impressions and emotional responses toward vaccines vary.
While some people feel comfortable with vaccination, others areapprehensive or hesitant because the COVID-19 vaccine is arelatively new technology and factors such as effectiveness andfuture risk are not yet known. Some regions and municipalities
are behind in their preparations for vaccination, causing
frustration or concern for some individuals. Whereas others may
not be concerned by some delays because of government
announcements of vaccination status, including the number ofpeople vaccinated per day. In addition, vaccination rates varyfrom country to country, possibly because of differences in theinterest and perception of vaccination among different national
populations. To increase a social acceptability of vaccinationinternationally, it is important to understand the emotional

XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE

Hiroko Shoji
Faculty of Science and Engineering
Chuo University
Tokyo, Japan
hiroko@kc.chuo-u.ac.jp

responses of people in various countries toward the vaccine andthe factors that cause these emotions.
We therefore analyzed the emotional responses expressed
regarding the COVID-19 vaccine over time in Japan, UnitedStates, Great Britain, Canada, Australia, and India, andinvestigated the differences and commonalities in the emotional
responses in these countries. The emotions followed Plutchik's
wheel of emotions [1] which includes eight primary emotions(Joy, Sadness, Anticipation, Surprise, Anger, Fear, Disgust, andTrust) and secondary emotions combined with specific primaryemotions. As a data source, we used data from tweets posted on
Twitter, which is one of the most popular tools for posting text
regarding individual situations and feelings in real timeinternationally. For an analysis of the emotions expressed in
tweets, we used the binary classifiers trained on the labeleddataset. From the classification results, we found that “Fear” wascommonly expressed in Japan, “Anger” and “Disgust” werecommonly expressed in the United States, Great Britain, Canada,
and Australia, and “Joy” was commonly expressed in India. We
then measured the correlation between changes in the daily ratioof tweets between two emotions and the similarity between two
emotions based on the classification results of the tweets toreveal the relationship between emotions expressed by people
regarding the vaccine. As a result, we confirmed that “Anger”
and “Disgust” were intrinsically correlated, and that “Joy” and“Anticipation” tended to be independent of each other in all
countries. Moreover, “Joy” and “Sadness,” which are located as
opposite emotions in Plutchik's wheel of emotions, exhibited apositive correlation in terms of the transitions of the daily ratioof tweets in Japan and Great Britain. These analyses are helpful
for recognizing the commonly expressed emotions towardvaccines internationally, and identifying the unique patterns ofemotions expressed in different countries.
To obtain information regarding positive changes inpeople’s feelings toward vaccine, we analyzed tweets in twocases: when the tweets with “Joy” increased in India, and when
the tweets with “Joy” and “Sadness” simultaneously increasedin Japan, using dependency analysis and burst detection. In theformer case, we found that the dissemination of information
about vaccines through an app developed by the Indian
government may be linked to “Joy.” In the latter case, we foundthe case that people expressed opposite emotions at the sametime, even in relation to the same event, depending on each
person’s situation. These findings will be useful for informing
approaches to promote vaccination worldwide.

TABLE I. TOTAL NUMBER OF TWEETS BY COUNTRIES

II. RELATED WORKIn recent years, many studies have analyzed changes inemotional responses toward COVID-19 and vaccination overtime. Hussain et al. [2] developed a classification approach for
identifying public concerns and sentiments toward the COVID19 vaccine in United States and United Kingdom, and conducted
a time-series analysis of sentiment change. Their approach used
BERT [3] and two lexical-based methods (VADER [4] andTextBlob [5]) to classify texts posted on Twitter and Facebook
along three axes: Positive, Neutral, and Negative. They thenanalyzed the relationship between world events and the increaseor decrease in the prevalence of text classified into eachemotional category over time, as well as the intensity ofemotions at the state or regional level. Yousefinaghani et al. [6]used VADER to make Positive or Negative decisions regarding
tweets about the COVID-19 vaccine in the United States andUnited Kingdom, and extracted keywords that appeared in eachsentiment. They then defined the categories “anti-vaccine,”“hesitant,” and “pro-vaccine,” and the keywords associated withthe categories for opinions about the vaccine, and analyzed thevolume of tweets for each category in each country. Hu et al. [7]analyzed how sentiments and opinions about vaccines havechanged in the United States by splitting the time-frame by twoevents regarding the vaccine that occurred between March 1,
2020 and February 28, 2021; clinical trials of Moderna and thefirst dose of the COVID-19 vaccine administered. They used
three sentiments (Positive, Neutral, Negative) in VADER andeight emotions in Plutchik's wheel of emotions from NRCLex[8], which is an emotion association dictionary for emotion
analysis, as well as using latent Dirichlet allocation [9] and word
cloud mapping for opinion analysis. Wang et al. [10] analyzed
controversial topics against opinions on mask wearing andvaccination using latent Dirichlet allocation in the United States.
In this approach, they analyzed how interest in the topic changed
using the sentiment score calculated by TextBlob, and to infer
the factors that caused the sentiment score to fluctuate bycomparing it to the actual events related to COVID-19. Our
study analyzes the differences and common elements in theemotions expressed by people in various countries. We also
obtained useful information for developing approaches toencourage the development of positive feelings toward the
COVID-19 vaccine by analyzing tweets from people expressing
positive feelings toward the vaccine.

CountryJapan
United States
Great Britain
CanadaAustralia
India

Total Number of Tweets
24,292,412
325,743
58,46247,09735,27432,337

excluded.
B. Classification Model
As a classification method for tweets, we used machinelearning by binary classification. We describe the dataset andparameter settings used to construct the classifiers, as well as theclassification performance for each emotion, below.


Dataset

For the classification of Japanese tweets, we used theJapanese emotion analysis dataset compiled by Kajiwara et al.
[12]. This dataset contains 43,200 texts posted on social
networking services labeled with Plutchik's eight emotions onfour levels of emotional intensity (none, weak, medium, andstrong). In addition, the labels included five types: one appliedby the writer of the text, three applied by three readers of the text,
and one by averaging the emotional intensities assigned by thethree readers. We used 40,000 data for training, 1,200 data forvalidation, and 2,000 data for evaluation. Labels representing
the average emotional intensity by the three readers were used,with weak, medium, and strong emotional intensity being
positive, and none being negative.
For the classification of English tweets, we used the English
emotion analysis dataset from the Emotion Classification (E-c)
task, which is included in the dataset distributed in SemEval
2018 Task 1: Affect in Tweets [13]. This dataset contains 6,838,
886, and 3,259 data sets for training, validation, and evaluation,
respectively, and each text posted on a social networking serviceis labeled with 11 different emotions (Anger, Anticipation,
Disgust, Fear, Joy, Love, Optimism, Pessimism, Sadness,
Surprise, Trust). We used eight of the eleven emotions (Anger,
Anticipation, Disgust, Fear, Joy, Sadness, Surprise, and Trust)to unify our classification with the emotion labels used in thedataset of Kajiwara et al.

III. ANALYSIS APPROACH



Parameter Settings

A. Collection of Tweet Data
To collect Japanese tweet data, we used the Twitter Search
API. The data were collected from June 13, 2021 to November
30, 2021, using “ワクチン(vaccine)” as the query. English tweet
data were obtained from the dataset1 constructed by DeVerna etal. [11]. This dataset contains IDs of tweets collected using 76
vaccine-related English keywords, divided by date until October
11, 2021. We analyzed the set of tweets from June 13, 2021 toOctober 11, 2021. We then classified tweets on the basis of thecountry code assigned to each tweet and included countries witha total of 30,000 or more tweets in our analysis. Table I shows
the number of tweets in each country. Note that retweets were

We used BERT [3] as a classification model. Parameters
were set to 32 for batch size, 5 for epochs, 1e-5 for learning rate,and 128 for the number of tokens, and Adam [14] was used for
optimization. For the learning model in the construction of theJapanese classifiers, we used a language model additionallytrained on the Japanese BERT. Specifically, we trained bertbased-japanese-whole-word-masking 2 using 3,000,000
Japanese tweets as training data and 300,000 Japanese tweets asverification data that were randomly selected from the tweets
from June 13, 2021 to November 30, 2021. For the learningmodel in the construction of the English classifiers, we used alanguage model additionally trained on the English BERT.

1

2

https://zenodo.org/record/5563154

https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking

Specifically, we trained bert-base-uncased 3 using 3,000,000
tweets as training data and 300,000 tweets as verification datathat were randomly selected from the tweets from June 13, 2021
to October 11, 2021 collected using tweet IDs in [11]TABLE II.

Joy
Sadness
Anticipation
Surprise
Anger
Fear
DisgustTrust

C. Classification Performance
Table II shows the classification performance for eachemotion in Japanese and English. These classifiers were used toclassify the tweets collected in Section III-A. Note that URL andusername beginning with @ were removed from the tweets, andeach tweet could be classified into multiple emotions because of
the binary classification of each emotion.
D. Our Approach for Comparing and Analyzing Emotions
Our analysis comprised two approaches: comparison ofrelationships between emotions based on the classification
results from the emotion classifier described in Section III-B,
and text analysis using a set of tweets classified as reflecting a
specific emotion. Each approach is described in detail below.
Comparison of Relationships Between Emotions

We measured the relationships between emotions on thebasis of the classification results of tweets in each country using
two calculation methods. One measured the correlation rate
between emotions by Pearson’s correlation coefficient. Thismethod was measured based on ratios of tweets expressing an
emotion to the daily set of tweets. The other method measured
the similarity between emotions using the Dice coefficient basedon two sets of tweets with emotion labels. Plutchik's wheel of
emotions defines secondary emotions by combining eight
different emotions. The higher the correlation between the two
emotions based on the transitions of the ratio of tweets and theresults of the tweet classification, the clearer people’s inherent
coexisting emotions toward COVID-19 vaccines. Combining
those emotions could then reveal more complex emotions.
Furthermore, comparison of the results of the analyses indifferent countries can elucidate the common emotions thatpeople in various countries share toward vaccines as well as theunique emotions expressed by people in each country.


Text Analysis Approach

Our text analysis method used the dependency relation andburst detection, and comprised two steps. First, dependency
analysis was performed for the tweets, and dependency
relationships were extracted from each tweet. Then, for eachemotion, burst detection for each dependency relation was
performed on the basis of the number of tweets that thedependency relationship extracted from the tweet set with theemotion and the number of tweets with the emotion label foreach day. Compared with word-based methods, this method wasable to more easily capture the factors that led to an increase in
tweets with a certain emotion. We used spaCy 4 as thedependency parsing tool, ja_ginza5 for the Japanese model, anden_core_web_sm6 for the English model. For burst detection,
we used Kleinberg’s method [15]. The parameters of
Kleinberg’s burst detection for Japanese and foreign tweets weremanually set to γ = 20, s = 2 and γ = 2, s = 2, respectively.

34

https://huggingface.co/bert-base-uncased
https://spacy.io/

CLASSIFICATION PERFORMANCE OF EACH EMOTION
Japanese
RecallPrecision
0.773
0.784
0.652
0.661
0.758
0.843
0.623
0.508
0.417
0.435
0.400
0.688
0.457
0.608
0.147
0.579

EnglishRecallPrecision
0.751
0.903
0.531
0.802
0.169
0.447
0.159
0.403
0.787
0.781
0.709
0.793
0.737
0.766
0.007
0.200

IV. ANALYSIS RESULTS
A. Classification Results and Comparison of Relationships
Between Emotions
Figs. 1 to 6 show the weekly average of the ratio of tweets
with an emotion to the daily number of tweets for the six
countries, respectively. The data shown in these figures suggestthat the main emotions toward vaccines in each country fall into
three types. Overall, “Fear” was commonly expressed in Japan.
In contrast, “Anger” and “Disgust” were commonly expressedin the United States, Great Britain, Canada, and Australia. “Joy”
was commonly expressed in India.
The Pearson’s correlation coefficient and Dice coefficientvalues between emotions in each country are shown in Tables
III through VIII. The values in the cells above the shaded line
are the Pearson’s correlation coefficients, and the values in thecells below the shaded line are the Dice coefficients. The data inthe tables suggest that all countries shared a strong correlation
between “Anger” and “Disgust” in the transition of the tweet
ratio, and a high rate of simultaneous labeling of tweets. Here,examining the combination of emotions in Plutchik's wheel of
emotions, the secondary emotion from the combination of“Anger” and “Disgust” is defined “Contempt.” Therefore, if
people expressed “Anger” or “Disgust” regarding a vaccine
event, they may simultaneously express the other emotion or“Contempt.” In countries other than India, in which thecorrelation between the transition of the ratio of tweets in “Joy”
and “Anticipation” was relatively high, the rate of simultaneouslabeling of tweets was not so high. In Plutchik's wheel of
emotions, the secondary emotion resulting from the combinationof “Joy” and “Anticipation” was “Optimism.” We infer thatpeople may not necessarily have optimistic feelings regarding
the vaccine. In Japan and Great Britain, the values of Pearson’s
correlation coefficients between “Joy” and “Sadness” wererelatively high (0.722 and 0.315, respectively), whereas Dicecoefficients were low (0.027 and 0.010, respectively) as shownin Tables III and V. From these results, it can be inferred that“Joy” and “Sadness” events regarding vaccines wereindependent, and that these events occurred simultaneously.
B. Text Analysis to Obtain Useful Information for
Encouraging Positive Feelings Toward Vaccines
In this section, we describe case studies of text analysis using
dependency analysis and burst detection to obtain useful
information regarding approaches for positively changing
56https://megagonlabs.github.io/ginza/
https://spacy.io/models/en

Fig. 1. Ratio of tweets with each emotion label in Japan.

Fig. 4. Ratio of tweets with each emotion label in Canada.

Fig. 2. Ratio of tweets with each emotion label in United States.

Fig. 5. Ratio of tweets with each emotion label in Australia.

Fig. 3. Ratio of tweets with each emotion label in Great Britain.

Fig. 6. Ratio of tweets with each emotion label in India.

emotional responses toward the COVID-19 vaccine. Here weshow the results of our analysis of “Joy” tweets in India, and inJapan, indicating that the ratio of “Joy” and “Sadness” tweets
increased simultaneously.

the dependency relations; (India, crossed), (vaccine doses, of),(the total number, crossed), and (the second highest country,
becomes), detected in the period from August 1 to August 6 inTable IX, were obtained from the vaccine-related news title,“India crossed the total number of vaccine doses administeredin the country. With the 47 crore doses administered, India
becomes the second highest country in the world to vaccinate
the masses.” The dependency relations; (at least one dose, got),(50%, got), and (Vaccine milestone, reached), detected in the
period from August 22 to August 28, were obtained from thenews title, “Vaccine milestone reached: 50% have got at least

First, Table IX shows examples of the dependency relations
in which bursts were detected during the periods of August 1 toAugust 6, August 22 to August 28, and September 12 to
September 18, 2021, when the ratio of “Joy” tweets peaked inIndia. From Table IX, we infer that tweets regarding vaccination
status are often posted. More detailed examination revealed that

TABLE III.
THE VALUES BY PEARSON’S CORRELATION
COEFFICIENT (UPPER RIGHT) AND BY DICE COEFFICIENT (LOWER LEFT)BETWEEN EMOTIONS IN JAPAN.

TABLE VI.
THE VALUES BY PEARSON’S CORRELATION
COEFFICIENT (UPPER RIGHT) AND BY DICE COEFFICIENT (LOWER LEFT)BETWEEN EMOTIONS IN CANADA.

TABLE IV.
THE VALUES BY PEARSON’S CORRELATION
COEFFICIENT (UPPER RIGHT) AND BY DICE COEFFICIENT (LOWER LEFT)BETWEEN EMOTIONS IN UNITED STATES.

TABLE VII.
THE VALUES BY PEARSON’S CORRELATION
COEFFICIENT (UPPER RIGHT) AND BY DICE COEFFICIENT (LOWER LEFT)BETWEEN EMOTIONS IN AUSTRALIA.

TABLE V.
THE VALUES BY PEARSON’S CORRELATION
COEFFICIENT (UPPER RIGHT) AND BY DICE COEFFICIENT (LOWER LEFT)BETWEEN EMOTIONS IN GREAT BRITAIN.

TABLE VIII.
THE VALUES BY PEARSON’S CORRELATION
COEFFICIENT (UPPER RIGHT) AND BY DICE COEFFICIENT (LOWER LEFT)BETWEEN EMOTIONS IN INDIA.

one dose.” The dependency relations; (India, sets), (PM’sbirthday, on), and (Covid-19 vaccines, for), detected in the
period September 12 to September 18, were obtained from
“With 2.5 crore jabs on PM’s birthday, India sets new record for
Covid-19 vaccines.” In addition, some of the tweets includingthese news titles included the text “via NaMo App,” which also
appeared in Table IX. NaMo App is the official distribution
system of Prime Minister Narendra Modi and provides the latest
information regarding the Prime Minister and his government.
The app also sends out news about vaccinations, and suchinformation can be tweeted on Twitter by users of the NaMoApp. In addition, examination of tweets extracted the relation (asingle day, in), detected from August 22 to August 26 and
September 12 to September 18, revealing that many tweets
praising the 100 and 200 million people vaccinated in a single
day were posted, respectively.

TABLE IX.

DEPENDENCY RELATIONS WHERE BURSTS WERE
DETECTED IN THE SET OF TWEETS CLASSIFIED AS “JOY” IN INDIA.

PeriodAugust1-6
August22 - 28
September
12 - 18

Dependency Relation(NaMo App, via), (India, crossed), (vaccine doses, of), (thetotal number, crossed), (the second highest country,
becomes)(NaMo App, via), (at least one dose, got), (50%, got),(Vaccine milestone, reached), (a single day, in)(NaMo App, via), (a single day in), (India, sets), (PM’sbirthday, on), (Covid-19 vaccines, for)In Japan, our analysis revealed that the ratios of “Joy” and“Sadness” tweets were increased in the period between July 25
and July 31, 2021. For the tweet sets classified as “Joy” and“Sadness” during this period, the dependency relation (ワクチン接種券，届い) ((vaccination ticket, delivered)) was included incommon in the dependencies extracted by the burst detection.

Here, when examining the tweets from which (vaccination ticket,
delivered) was extracted, we found that in “Joy” tweets, manyusers reported that they had received a vaccination ticket.
Conversely, in “Sadness” tweets, there were many tweets about
not having received the vaccination ticket, or having received it
but not being able to make an appointment, which was different
from “Joy.” We also examined the time-series trends of theoccurrence of tweets with (vaccination ticket, delivered) in the“Joy” and “Sadness” tweets. The daily ratios of tweets
expressing “Joy” and “Sadness” are shown in Fig. 7. It can beseen that the ratios of tweets in both emotions peaked in late Juneand early July, and that the transitions of both emotions were
almost identical. These results suggest that the opposite
emotions may be simultaneously increased, even in response to
the same event, depending on people’s physical or psychologicalsituation, and that this is one of the reasons for the highcorrelation coefficient between “Joy” and “Sadness” shown in
Table III. Moreover, in the event of the dispatch of vaccination
ticket, the improvement in the distribution of vaccination ticket
and reservation system could lead to an increase in the numberof people who express secure and positive feelings about
vaccines from the two types of tweet sets that expressed the
opposite emotions of “Joy” and “Sadness.”
V. CONCLUSION
We analyzed the differences and commonalities inemotional responses toward COVID-19 vaccination amongpeople in Japan, the United States, Great Britain, Canada,
Australia, and India by classifying tweets and using Pearson’s
correlation coefficients and Dice coefficients. Our analysis
results revealed that “Fear” was commonly expressed in Japan,“Anger” and “Disgust” were commonly expressed in the UnitedStates, Great Britain, Canada, and Australia, and “Joy” wascommonly expressed in India. The results also suggested an
intrinsic correlation between “Anger” and “Disgust,” whereas“Joy” and “Anticipation” tended to be independent of each other.
In addition, “Joy” and “Sadness,” which are represented as
opposite emotions in Plutchik's wheel of emotions, wereconfirmed to be positively correlated in terms of the transitionsof the ratios of tweets in Japan and Great Britain.
We additionally analyzed the “Joy” tweet set in India and the“Joy” and “Sadness” tweet sets in Japan using dependency
analysis and burst detection. In India, we found that thedissemination of information about vaccines through an Indian
government application could be linked to “Joy.” In Japan, we
found that (vaccination ticket, delivered) was detected for both
emotions. We analyzed the tweets from which we extracted eachdependency relation in more detail, and found that many “Joy”
tweets reported that “I had received the vaccination ticket” inresponse to the event of the delivery of the vaccination ticket,”and many “Sadness” tweets reported that “I have not received
my vaccination ticket” and “I received my vaccination ticket,
but I can’t make an appointment.” These results suggest that thesame event can simultaneously arouse opposite emotions,depending on each person’s situation. In addition, our findings
indicated that solving the problems described in “Sadness”
tweets could lead a person to express “Joy” instead. Thisapproach is useful for obtaining useful data to inform
approaches for encouraging positive feelings toward vaccination.

Fig. 7. Transitions in the ratio of tweets expressing “Joy” and “Sadness”
from which (vaccination ticket, delivered) were extracted.

ACKNOWLEDGMENT
This work was supported by JSPS KAKENHI Grant Number JP22K18152.

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