An Analysis of Shifts in Public Interests andSentiments in Japan Using News Tweet Data during
the COVID-19 Pandemic
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—Identifying the topics that interested and moved
people during the COVID-19 pandemic can shed light onsocietal trends. Uncovering these topics can answer questions
such as what induced positive feelings in a climate of widespreadanxiety caused by COVID-19? How did COVID-19-induced
anxiety change? Did other topics induce more anxiety than
COVID-19? We analyzed shifts in people’s interest in andsentiments about topical events that reported in Japan during
the COVID-19 pandemic from February 2020 to May 2022. We
did so using tweets posted by news sites on Twitter and replies
to those tweets. Specifically, we performed topic analysis onnews tweets to identify topics focusing on real-world events. We
then counted the number of replies to each tweet on a topic andvisualized how people’s interests changed. We performed
sentiment analysis on reply tweets to assess whether people
expressed positive or negative feelings about the news andexamined how each topic evolved according to the numbers of
positives and negative responses. We found that the Tokyo
Olympics and celebrity marriages were hot topics prompting
positive sentiments, negative sentiments may be shifting fromtopics related to COVID-19 to the Ukraine–Russia conflict, andpeople’s interests may be primarily in negative events that evoke
negative feelings.
Keywords—Sentiment Analysis, Topic Analysis, Twitter

I. INTRODUCTION
The COVID-19 outbreak that first emerged in December
2019 was one of the most transformative events of 2020 andbeyond, with the topic of new COVID-19 infections
dominating the news. Between January and March 2020,
when the topic of COVID-19 first captured public attention,
COVID-19 aroused negative feelings [1] and a great deal ofanxiety for many people. Subsequently, reports on thenumbers of new positive COVID-19 cases and the promotionof mask wearing and vaccinations against the COVID-19
gained momentum, arousing public interest and sentiments
relating to these topics on a daily basis. At the same time,
major global events occurred beyond COVID-19, notably the
Tokyo Olympics in 2021 and the Ukraine–Russia conflict in2022. In addition to these events, various domestic events andincidents occurred that were also reported. Therefore, people’sinterests and sentiments may have shifted to various topics
other than those related to COVID-19. We aimed to ascertain
people’s interests, the topics that induced positive andnegative feelings during the COVID-19 pandemic. This
analysis can be shed light on overall social trends during the
pandemic, the topics that induced positive feelings, and theevents that were viewed more negatively than COVID-19. In
addition, identified events that evoked positive feelings
among many people in an anxiety-inducing context such as

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

the pandemic can be widely shared within society and may
serve as a resource for reducing psychological anxiety.
We used news tweets and reply tweets to those tweets
posted on Twitter as our data for to analyzing topics that rose
to prominence during the COVID-19 pandemic and people’sinterests and feelings regarding these topics. In recent years,
with the advance of the Internet and the spread of social
networking services (SNS), various news sites have procured
official accounts on Twitter to disseminate information more
widely. It is now easy to post comments and opinions about
the news topics posted on their news accounts as replies on
Twitter. Topic analysis can be performed on tweets posted bynews sites to clarify topics about real-world events. Moreover,the number of replies on each topic can be aggregated andvisualized to examine how people’s interests have changed. In
addition, sentiment analysis on a set of reply tweets can be
performed to examine whether positive or negative feelings
were expressed on the news topic. Thus, it is possible to obtainan overview of topics that have aroused people’s feelings
without analyzing all tweets. Furthermore, the news articles
on the events that triggered people’s interest and feelings
about a particular topic can be easily referenced, as each tweet
posted on the news account is directly linked to the reply
tweets. Here, we demonstrate the effectiveness of ouranalytical approach in conducting a multifaceted analysis
using news tweets and reply tweets posted by Yahoo! News,
one of the best-known news sites in Japan, on Twitter during
an extended period from February 2020 to May 2022.
Through the experiments, we found that the Tokyo Olympics
and celebrity marriages were hot topics prompting positive
sentiments, and people’s interests and negative sentiments
may be shifting from topics related to COVID-19 to the
Ukraine–Russia conflict.
II. RELATED WORK
Many studies have analyzed changes in people’s feelings
relating to COVID-19. Saito and Haruyama [ 2 ] analyzed
sentimental shifts in residents of New York, Los Angeles, andChicago in response to infection conditions and state
government orders during the COVID-19 pandemic. The
authors examined the comparison between sentimental
changes and the number of new positive COVID-19 cases andrelated events. Hussain et al. [3] developed an approach for
identifying and categorizing public concerns and sentiments
toward the COVID-19 vaccine in the United States and United
Kingdom, Moreover, they conducted a time-series analysis ofchanges in sentiments. Wang et al. [4] analyzed controversial
topics related to mask-wearing and vaccination in the UnitedStates using Latent Dirichlet Allocation (LDA) [5], examined
how interest in the topics analyzed changed over time usingsentiment scores calculated by TextBlob [6], and considered
the factors that were caused scores to fluctuate.
Other studies have analyzed both news and tweets. De
Melo and Figueiredo [7] performed a sentiment analysis of the
impact of COVID-19 in Brazil using news articles and tweets
to identify changes in sentiment and topics over time. Evanset al. [8] compiled news tweets about COVID-19 posted on
the Twitter accounts of several news media outlets in the
United Kingdom and reply tweets in response to them. They
analyzed topics relating to COVID-19 using LDA model andperformed sentiment analysis using RoBERTa model [9] foreach tweet set to identify emotions in the news and users. Our
study differs from these other studies in that it does not only
analyze changing sentiments among people interested in
topics related to COVID-19. Instead, it focuses on a variety of
topics reported by news sites about real-world events todetermine how societal interests and emotions as a whole haveshifted.
III. METHODOLOGYOur analytical approach entailed four components: (1)collecting news tweets posted on Twitter news accounts andreply tweets; (2) topic analysis performed on the set of news
tweets; (3) sentiment analysis conducted on the set of reply
tweets; and (4) visualization and analysis of changes inpeople’s interests and sentiments using the results of the topic
and sentiment analysis. Each of these steps is described indetail below.
A. Tweet Compilation
News tweets posted on a Twitter news account and reply
tweets in response to them were collected via the programinterface of the Twitter application. We selected Yahoo! News
(username: @YahooNewsTopics) as the news site account forour study. Yahoo! News provides information disseminatedby various newspapers and news agencies in Japan and is a
popular news service in Japan. By analyzing news tweets
posted on the Yahoo! News Twitter account and tweets in
response to them, we obtained an overview of topics that
occurred in Japan and people’s sentimental responses to them.
The collection period was from February 1, 2020, to May 31,
2022.
After compiling the news tweets, we removed the URL
and hashtag from each news tweet, and the URL from each
reply tweet. News tweets in which the content sentences weredeleted as a result of this operation were excluded. The
number of reply tweets for each news tweet was counted, andnews tweets with less than 20 reply tweets were considered asless newsworthy as a viewpoint of the people’s interest andtherefore excluded. The numbers of news tweets and reply
tweets compiled were 31,530 and 2,780,110, respectively.
B. Topic Modeling for News Tweets
To perform a topic analysis on the news tweet set, we used
BERTopic 1 [ 10 ], which can handle short sentences
represented by an embedding model using Sentence-BERT[ 11 ]. For the clustering model in BERTopic, we used
HDBSCAN [12 ]. The minimum topic size was set at 16,
which was 0.05% of the total number of news tweets. As apreprocessing, we conducted the morphological analysis foreach news tweet. The Japanese morphological analyzer,
12

https://maartengr.github.io/BERTopic/index.html
https://github.com/ids-cv/wrime

MeCab, was used to divide news tweet sentences intomorphemes. News tweets considered as outliers using
BERTopic were excluded from the subsequent analysis.
Ultimately, 229 topics were generated for the news tweet set.
After conducting the topic analysis, we extracted thecompound nouns for each topic using c-tfidf to represent thegenerated topics.
C. Sentiment Analysis on Reply Tweets
For the sentiment classification of the reply tweets, weused the WRIME dataset (version 2)2 [13][14]. This dataset
contains 35,000 texts posted on SNS labeled with Plutchik’seight emotions [ 15 ] on four levels of emotional intensity(none, weak, medium, and strong), and sentimental polarity
on five levels (strong negative, negative, neutral, positive,
strong positive). They also covered five types of labels: one
applied to the writer of the text, three applied to three readersof the text, and one that was the average of the emotional
intensities assigned by the three readers. According to theTrain/Dev/Test distribution on the WRIME dataset, we used
30,000 pieces of data for training, 2,500 pieces of data forvalidation, and 2,500 pieces of data for evaluation. We appliedlabels representing the average emotional intensity identified
by three readers. Emotional polarity labels were used to
examine shifts in people’s positive and negative feelings
during the COVID-19 pandemic. To construct the positiveclassifier, we used strongly positive and positive labels toillustrate positive examples, and to construct the negativeclassifier, we used strongly negative and negative labels toillustrate positive examples.
BERT [16] was used as the classification model with thefollowing parameters: 32 for batch size, 10 for max epochs,
1e-5 for learning rate, and 128 for the number of tokens. The
Adam method [17] was used for optimization. We used the
BERT-based-Japanese-whole-word-masking model as alearning model3. Table I shows the precision and recall values
for the positive and negative classification, respectively.
These classifiers were used to determine whether each reply
tweet was positive/negative.
D. Visualization and Analysis of Shifts in People’s Interests
and Sentiments
Figure 1 depicts the workflow in the process of visualizingchanges in people’s interests and sentiments about topics
relating to real-world events. The steps in the process aredescribed below.
1) Visualization of shifts in people’s interests
We regarded the number of reply tweets as the degree ofinterest in the news (topic), and measured people’s interest ineach topic as follows. First, for each topic identified in the
topic analysis, news tweets were arranged chronologically bymonth. Next, the tweets in response to the news tweets werecounted, and the total value was calculated for each month.
2) Visualization of shifts in people’s sentiments
In our study, by identifying whether each news was
positive or negative for people, we judged what people had
positive/negative feelings about the topic to which the news
belonged. First, we identified whether the news was received
as positive or negative. Specifically, for each news tweet, the3

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

TABLE I.

CLASSIFICATION PERFORMANCE

Precision
Positive
Negative

Recall
0.780
0.727

0.799
0.756

(a) Visualizing shifting public interest in a topic(b) Visualizing shifting positive sentiments toward a topic(c) Visualizing shifting negative sentiments toward a topic
Fig. 1. Shifts in people’s interest, and their positive and negative sentiments
toward a topic. Note: news tweets for which the ratio of the number of
positive (negative) tweets to the number of reply tweets was ≥0.3 wereregarded as positive (negative).

ratio of the number of tweets with positive or negative
responses to the number of reply tweets was measured, andthose with a ratio of or above 0.3 were considered positive or
negative news tweets. Next, for each topic, we sorted positiveor negative news for each month. Last, we counted thepositive or negative reply tweets and calculated the total value
for each month.
IV. RESULTS
We present the results obtained using our analytical
approach and analyze changes in people’s interests andsentiments during the COVID-19 pandemic in Japan. The

analysis was performed from three perspectives: (1) shifts inpeople’s interests and sentiments during the period fromFebruary 2020 to May 2022; (2) the relationship between thenumber of news tweets and the number of reply tweets; and(3) correlations between interest and positive and negative
sentiments. Note that topic numbers labeled using BERTopic
were used for identified topics.
A. Analysis of the People’s Interests and Sentiments
Tables II, III, and IV respectively show the top 10 topics
with the highest numbers of reply tweets, positive tweets, andnegative tweets, respectively, during the period from February2020 to May 2022. Figures 2, 3, and 4 respectively showchanges in the numbers of reply tweets, positive tweets, andnegative tweets for the top 10 topics per month. Using theresults shown in Table II and Figure 2, we first examinedpeople’s interests. Table II shows that people’s main interests
were broadly divided among the following categories: topics
related to COVID-19 (Topics 1, 2, 5, 8, 3, 6, 23); the Ukraine–
Russia conflict (Topic 0); and marriage and dating (Topic 4).
Figure 2 also indicates that topics related to COVID-19
attracted interest from time to time until January 2022.
Specifically, from February to May 2020, key topics covered
the mask (Topic 5), closure of the school due to the spread of
COVID-19 (Topic 2), and requests for closure of restaurants
due to the emergency declaration (Topic 6) and benefit of the
100,000 yen (Topic 23). In July, November, and December
2020, discussions and trends relating to GoTo Travel, agovernment-led campaign to revitalize the travel industry anddomestic travel after economic losses, were reported (Topic
8). Beside this, since the beginning of 2020, the media also
covered the number of new positive cases of COVID-19
reported on an almost daily basis along with increases anddecreases in the number of people who tested positive for thevirus. Topic 3 is likely that people’s interest in this topic
increased because COVID-19 was an unknown virus, and thestate of emergency and self-restraint and confinement ofactivities in 2020 were new experiences for many Japanesefrom April to July 2020. Subsequently, from May to
September 2021, there was considerable discussion about the
COVID-19 vaccine (Topic 1). We checked the news tweets
categorized under Topic 1 during this period, and revealed thatthe focus was mainly on trends related the government’vaccination and environmental improvement initiatives andnews about vaccination status. In October 2020, Topic 4,
which is not related to COVID-19, emerged as a hot topic. We
checked the news tweets categorized under Topic 4 during thismonth, and revealed active coverage of several issues related
to the marriages of members of the royal family. In November
2021, Topic 23, which attracted attention in April 2020, once
again emerged as a hot topic. A comparison of news tweets
categorized under Topic 23 in April 2020 and November 2021
showed that the government’s restrictions on who could or
could not receive the benefits and the 100,000-yen handout
were hot topics of discussion in the news during both periods.
Then from February 2022, people’s interests focused on
the Russia-Ukraine conflict. Apart from this topic, mask
(Topic 5) and school (Topic 2), which were prominent topics
in February 2020, were also slightly prominent in May 2022.
An examination of the news tweets categorized into topics
revealed that a lot of news tweets posted in and aroundFebruary 2020 focused on Topic 5 covering mask shortages
and resale issues. However, in May 2022, the most
newsworthy topic had shifted to daily mask wearing. In
February 2020, Topic 2 covering temporary school closures

TABLE II.

Topic
0
1
2
5
4
8
3
6
7
23

TOPICS OF INTEREST, WORDS, AND THE TOTAL NUMBER
OF REPLY TWEETS

Examples of words
Russia, Ukraine, Russian military, Ukraine
invasion, President Putin, invasion, attack,President Zelensky, Russia, blame
vaccine, vaccination, new coronavirus vaccine,
third dose, adverse reaction, elderly, local
government
school, faculty, closed, student, temporaryclosed, primary school, guardian, university,
high schoolmask, cloth mask, wearing, distributing,wearing masks, Abenomask, outdoor, allhouseholds, recommended
marriage, divorce, actress, relationship,
husband, affair, public woman
GoTo Travel, tourism support program, travel,suspension, GoTo Campaign, campaign,business, government, exclusion
Tokyo, new, infected, confirmed, new type ofcoronavirus, severely ill
restaurant, store, closure request, closing,closed, liquor, business hours, department store
layoffs, companies, stop, employees, bonuses,
voluntary retirement, recruitment, jobs, state
employees, telecommuting
benefit, 18 years old, 100,000 yen equivalent,
income limit, economic stimulus, cash benefit,
payment, 100,000 yen, children

No. ofreplies
85,887TABLE III.

Topic
4
41

85,784
16
77,98230
73,61338
64

45,75037,132

2

34,801

25

34,719030,745
19

TOPICS AROUSING POSITIVE FEELINGS, WORDS, AND THE
TOTAL NUMBER OF POSITIVE REPLY TWEETS

Examples of words
marriage, divorce, actress, relationship,
husband, affair, public woman
first child, birth, pregnancy, report, second
child, first child pregnancy, talent, actress,
mother and childdeath, cartoonist, actor, voice actor,
pneumonia, hospital, active, obituary, 81
starring, drama, movie, broadcast, cast, actor,
actress, protagonist
this week, featured, topic, marriage, Wannouncement, 3400 comments
women’s curling, defeated 400m individual
medley, gold medal, silver medal, falls,
protection
school, faculty, closed, student, temporaryclosed, primary school, guardian, university,
high school
sales, pork, lunch, cooking, popular, launch,
cup noodles, luxury bread, shopkeepers, eggs
Russia, Ukraine, Russian military, Ukraine
invasion, President Putin, invasion, attack,President Zelensky, Russia, blame
broadcast, end, NTV, TBS, curtain, MC, start,
history, Fuji TV, information program

No. ofreplies
5,302
2,646
2,483
2,129
2,060
1,786
1,555
1,476
1,091
1,088

29,991

Fig. 3. Shifts in people’s positive feelings indicated by the number of
positive reply tweets in response to positive news tweets
Fig. 2. Shifts in people’s interests indicated by the number of reply tweets

and cancellation of graduation ceremonies as a countermeasure against COVID-19 featured prominently in the news.
However, in May 2022, public interest focused on a variety ofnews stories that were not related to COVID-19, such as news
about the banning nicknames in schools, the problem of thegym sitting, and bullying issues.
Next, we examined expressions of positive feelings. As
shown in Table III, topics that tend to convey positive
feelings, for example, those related to marriage and romance(Topic 4, Topic 38) and childbirth (Topic 41) were ranked
highest. As Figure 3 shows, various topics were prominent at
each month. In addition, the Tokyo Olympic was held from
July 23 to August 8, 2021, and Topic 64, which is a sportsrelated topic, aroused excitement. This topic also sparked
excitement in February 2022 by the Beijing Olympics. These
results suggest that even when anxiety was widespread withinsociety, as was the case during the COVID-19 pandemic,
people tended to respond honestly to any positive event.
Sharing these results with a larger number of people could
lead to a slight abatement of the prevailing sense of anxiety.

However, it is striking that Topic 16 concerning the death ofcelebrities appeared at the top of the list of positive events. To
explore this finding further, we examined news tweets thatwere categorized under this topic and found that when, forexample, a film director or a cartoonist died, Twitter users
tweeted their memories about this individual’s work, while atthe same time praying for the repose of their soul. Also, whenan athlete or celebrity died, they recalled past scenes when thecelebrity was at their peak. It is also noteworthy that Topic 0,
which appears in Table II, and IV, also appears at the top of
Table III. An examination of the news tweets categorizedunder Topic 0 confirmed relatively positive responses to somenews in the enlistment of volunteers in the Ukrainian army,
statement by President Zelensky and donations/support for
Ukraine whereas there was a negative reaction to most of thenews. In addition, Topic 2, which appears in Table II, isreflected in both the positive and negative responses in Tables
III and IV, respectively. Our analysis of news tweets related
to the COVID-19 categorized under Topic 2, which wereposted in February to April 2020, revealed that news about
temporary school closings and cancellation of graduation
ceremonies were deemed negative, whereas some news about

TABLE IV.

Topic
1
2
0
5
8
6
3
4
7
23

TOPICS AROUSING NEGATIVE FEELINGS, WORDS, AND THE
TOTAL NUMBER OF NEGATIVE REPLY TWEETS

Examples of words
vaccine, vaccination, new coronavirus vaccine,
third dose, adverse reaction, elderly, local
government
school, faculty, closed, student, temporaryclosed, primary school, guardian, university,
high school
Russia, Ukraine, Russian military, Ukraine
invasion, President Putin, invasion, attack,President Zelensky, Russia, blame
mask, cloth mask, wearing, distributing,wearing masks, Abenomask, outdoor, allhouseholds, recommended
GoTo Travel, tourism support program, travel,suspension, GoTo Campaign, campaign,business, government, exclusion
restaurant, store, closure request, closing,closed, liquor, business hours, department store
Tokyo, new, infected, confirmed, new type ofcoronavirus, severely ill
marriage, divorce, actress, relationship,
husband, affair, public woman
layoffs, companies, stop, employees, bonuses,
voluntary retirement, recruitment, jobs, state
employees, telecommuting
benefit, 18 years old, 100,000 yen equivalent,
income limit, economic stimulus, cash benefit,
payment, 100,000 yen, children

No. ofreplies
43,13240,94940,30937,534
19,871
17,625
17,314
15,39915,36014,498Fig. 4. Shifts in people’s negative feelings indicated by the number ofnegative reply tweets in response to negative news tweets

supports to students to prevent COVID-19 transmission weredeemed positive. Thus, and as seen in the large difference inthe number of positive and negative tweets in Topics 0 and 2
shown in Tables 3 and 4, we found that people mainly showednegative feelings to these topics, however positive feelings tosome news.
An analysis of the negative feelings conveyed in Table IV
and Figure 4 showed that the distribution of topics andtransitions in topics were similar to those of people’s interests
depicted in Table II and Figure 2. Interestingly, Topic 4,
covering marriage and dating reports, appeared in both Tables
III (positive) and IV (negative). However, unlike the shift for
positive feelings, Topic 4 rose in prominence in only October
2021. Our examination of the negative news tweets in October
2021 revealed negative feelings of several issues related to themarriages of members of the royal family. From these results,
it can be posited that public anxiety during the pandemic
shifted from events related to COVID-19 to marriage issues
concerning the royal family, subsequently focusing on the
Ukraine–Russia conflict.

B. The Relationship between the News Tweets and the
Reply Tweets
It is natural that a lot of news related to a topic that attracts
people’s interest or makes them feel sentimental is taken up.
That is, it can be said that the number of replies increases as
the number of news classified into topics increases. To
investigate whether the number of reply tweets on a specifictopic depended on the posting of news tweets, we investigated
the relationship between the number of news tweets on a
specific topic. We here examined the topics shown in Tables
II, III, and IV and measured correlation between news andreplies in the topic using Pearson’s product-moment
correlation based the number of news tweets and reply tweets
on each month. The results are shown in Table V.
Table V reveals that the overall correlation value was high,and the numbers of news tweets and reply tweets were
proportional. In particular, the value of the correlation
coefficient for almost all topic in interest and negative
exceeded 0.8. These results suggest that there is a correlation
between the amount of media coverage on COVID-19-related
topics and the Ukrainian-Russian conflict, and public interest
and negative sentiment. In positive, unlike interest andnegative, it tends to be low. In particular, Topic 30 covering
movies had correlation coefficients below 0.25. This is likely
due to the high positive sentiment toward certain events in themovie and drama topic. On the other hand, Topic 64 related tosports had a very high value of 0.951. It is thought that thiswas because the Olympics-related news was actively covered,and in conjunction with that, many people showed positive
feelings about the athletes’ results and their progress.
C. Correlation between Interest and Positive and Negative
Feelings about Topics
As mentioned above, from Figures 2, 3, 4 and Tables 2, 3,
4, it appears that people’s interests are more similar in thenegative than in the positive. We investigated the relationshipbetween people’s interest in a topic and their positive andnegative feelings about it. To do so, we counted the numberof reply tweets for each topic under three categories: interest,
positive, and negative. We then measured the correlation
coefficient using Spearman’s rank correlation between two ofthem. As a result, we obtained correlation values of 0.285 forinterest and positive feelings, 0.943 for interest and negative
feelings, and 0.090 for positive and negative feelings. These
results suggest that people’s interest during the COVID-19
pandemic may be associated with the negative feeling morethan positive feeling.
V. CONCLUSION
We analyzed shifting topics of interest and sentiments asconveyed by the Japanese public during the period of the
COVID-19 pandemic. We did so by applying topic analysis
and sentiment analysis to news tweets posted by Yahoo! Newson their Twitter account and replies to them. We examined
trends in people’s interests in and sentiments toward each
topic according to the number of replies. This study was
conducted over an extended period from February 2020 to
May 2022. During this period, positive feelings wereexpressed about the Tokyo Olympics, celebrity marriages, andlove-related topics. Beside this, COVID-19-related topics
dominated the news in terms of public interest and negative
feelings until January 2022. However, beginning in February2022, the Ukraine–Russia conflict was the main focus ofTABLE V.
PEARSON’S PRODUCT-MOMENT CORRELATION
COEFFICIENT CALCULATED FOR THE NUMBER OF NEWS TWEETS AND REPLYTWEETS FOR EACH TOPIC

Topic
0
1
2
5
4
8
3
6
7
23

Interest
Value
0.985
0.956
0.859
0.960
0.843
0.943
0.813
0.969
0.628
0.818

Topic
4
41
16
30
38
64
2
25
0
19

Positive
Value
0.618
0.621
0.838
0.224
0.812
0.951
0.767
0.644
0.892
0.799

Negative
Topic
Value
1
0.958
2
0.857
0
0.983
5
0.956
8
0.942
6
0.967
3
0.799
4
0.883
7
0.586
23
0.826

attention. Our analysis of the correlation between public
interest, positive feelings, and negative feelings indicates that
there may be strong correlation between people’s interest andtheir negative feelings. In the future, we plan to undertake aanalysis in which we compare shifts in topics of interest andsentiments in other countries with those found in Japan.
ACKNOWLEDGMENT
This work was supported by the Chuo University Research
Cluster Formation Support Program in FY2021 and JSPS
KAKENHI Grant Number JP22K18152.
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