Changes In Sentiment Research Articles

Analyzing Capital Flow Drivers Using the ‘At-Risk’ Framework: South Africa’s Case

Cross-border capital flows are important for South Africa. They fund the nation’s relatively large external financing needs and have important financial stability implications evidenced by the large capital outflows and asset price selloffs during the COVID-19 pandemic. This paper adds to the literature on the drivers of South Africa’s capital flows by applying the ‘at-risk’ framework––which differentiates between the likelihood of “extreme” inflows (surges) and outflows (reversals) and of “typical” flows––to both nonresident and resident capital flows. Estimated results show that among nonresident flows, the portfolio debt component is most sensitive to changes in external risk sentiment particularly during reversals. This applies to flows to the sovereign sector. Nonresident equity flows, both portfolio and FDI, are most sensitive to domestic economic activity especially during surges. This applies to flows to the corporate and banking sectors. Results also suggest that resident flows, in particular the FDI component, tend to offset nonresident flows, thus acting as buffers against funding withdrawal during periods of global risk aversion.

Share pledge transactions as an investor sentiment indicator - Evidence from China

This paper examines how share pledge transactions signal changes in investor sentiment. Based on the investor sentiment model proposed by Gervais and Odean (2001), we develop an investor sentiment model by incorporating the impact of share pledge transactions on stock prices by linking the signal transmission of the share pledges with investor decision making. Using data from the China stock market from 2014 to 2018, we find that share pledge transactions can lead to emotional bias and consequently affect investment decisions. Specifically, stock market volatility increases with changes in both optimistic and pessimistic investor sentiment bias regarding share pledge transactions. From the perspective of behavioral finance, with the Vector Autoregression (VAR) model and the impulse response test, we study the relationship between investor sentiment indicators, including share pledge factors and stock market volatility. Among the investor sentiment indicator components, we find that the composition weights of the share pledge volume and the share pledge ratio are 22.54 %, and 21.49 % respectively. Share pledge transactions have an obvious impact on investor sentiment. The paper also shows that share pledge transactions have an accelerating effect on stock price cyclical fluctuations through the investor sentiment effect. During a stock index crash, such transactions may constitute a potential market factor of instability.

A Study of Multi-Scale Relationship Between Investor Sentiment and Stock Index Fluctuation Based on the Analysis of BEMD Spillover Index

Abstract According to behavioral finance theory, investor sentiment generally exists in investors’ trading activities and influences financial market. In order to investigate the interaction between investor sentiment and stock market as well as financial industry, this study decomposed investor sentiment, stock price index and SWS index of financial industry into IMF components at different scales by using BEMD algorithm. Moreover, the fluctuation characteristics of time series at different time scales were extracted, and the IMF components were reconstructed into short-term high-frequency components, medium-term important event low-frequency components and long-term trend components. The short-term interaction between investor sentiment and Shanghai Composite Index, Shenzhen Component Index and financial industries represented by SWS index was investigated based on the spillover index. The time difference correlation coefficient was employed to determine the medium-term and long-term correlation among variables. Results demonstrate that investor sentiment has a strong correlation with Shanghai Composite Index, Shenzhen Component Index and different financial industries represented by SWS index at the original scale, and the change of investor sentiment is mainly influenced by external market information. The interaction between most markets at the short-term scale is weaker than that at the original scale. Investor sentiment is more significantly correlated with SWS Bond, SWS Diversified Finance and Shanghai Composite Index at the long-term scale than that at the medium-term scale.

Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective.

BackgroundThe COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance.ObjectiveThe aim of this study was to investigate public opinion and perception on COVID-19 vaccines in the United States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter.MethodsWe collected over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes.ResultsAn increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors help identify underlying themes and validate insights from the analysis.ConclusionsThe analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics, and promote the confidence that individuals within a certain region or community have towards vaccines.

The marketing strategy of online video based on danmaku-video: A bimodal analysis

<p id="p00005">As the rapid development of Internet, danmaku video emerges. This user-video interactive mode has new features, such as real-time dynamic emotion expression and multimodal emotion distribution. Meanwhile, the new features also bring challenges to practical research work, for instance, user portrait depiction is more difficult, video recommendation and advertising need to be more accurate. Existing research has not yet deeply analyzed new features of danmaku video, nor fully explored the academic research value for itself. Based on the theories of psychology, marketing, and some other interdisciplinary frontier knowledge, as well as combined with deep learning, natural language processing technology and system dynamics method, this study attempts to analyze and model danmaku video data from a data-driven perspective, for deeply mining the potential business value of video big data. The objective of the study is to create a high-quality online video intelligent marketing platform. A sentimental recognition approach is proposed to capture the internal relationship between sentimental features of danmaku text and visual sentimental features of video, so as to represent users’ sentiment. Considering the influence of users’ dynamic emotion on behavior, the dynamic user portrait with sentimental features is constructed to describe full picture of user characteristics, as well as to explore the access logic and behavioral preferences that are hidden behind behavior data. By positioning user roles, the sticky marketing mechanism of online video platform is established. Furthermore, focused on different advertisement types, like creative ads, interstitial ads and oral ads, this study explores the impact of video ads on the emotional and psychological changes of users, reveals the correlation between users’ sentiment changes and video ads insertion mode, and thus puts forward the “content customized” dynamic advertising strategy for danmaku video. It not only enriches the existing research, but also provides theoretical guidance and decision-making support for online video platforms to accurately locate and analyze user demands. In particularly, the contributions of the study include: (1) Previous research on marketing strategy of online video platform mainly focused on platform pricing, brand reputation and user satisfaction, ignoring external stimulus factors, such as dynamic factors of user behavior and emotional changes caused by danmaku video advertising. This paper combines big data of danmaku video to analyze users’ emotional responses to the external stimuli. Based on multimodal attention fusion, the hierarchical deep association co-attention model is designed to achieve users’ sentiment recognition. The paper reveals the correlation between video ads and users’ emotional changes, proposes the dynamic intercutting strategy of customized video ads, innovates the video advertising marketing model, and helps advertisers to enhance the brand marketing value; (2) The traditional method of constructing user portrait does not focus on the strong correlation among users’ sentiment, natural attribute and behavior. To this end, the study is driven by the sticky marketing of online video platform. Based on three dimensions of users’ sentiment, natural attribute and behavior, a dynamic user portrait model with four modules of basic data, behavior modeling, service application and evaluation feedback is established. Considering user demand and dynamic user portrait, the model of sticky marketing mechanism is constructed to recommend rich content options for users, to increase users’ stickiness and to prolong users’ usage time in online video platform. Thus, it forms a benign marketing mode of flow closed loop and completes the flow transformation between user value and content value in online video platform. Through this study, user needs will be met from a higher quality level, the accuracy of video advertising will be comprehensively improved, and the marketing value can be maximized in online video platform.

Assessing the reversal of investor sentiment

Assessing the reversal of sentiment in stock markets is needed because, according to the social mood cycle, the change of social mood over time is an antecedent of price movements. The purpose of this study is to empirically assess reversal of investor sentiment, to show the phases of social mood cycle from increasing mood to decreasing mood, and to explain the dynamic change in market inefficiency from increasing to decreasing. Growth modeling, developed particularly for dealing with the change over time, is used in this study for assessing the reversal of investor sentiment. The autocovariance structure of errors and the variances/covariances of the random coefficients are all taken into account in the model. The results have indicated that the change in investor sentiment over time is inverted U-shaped for the entire market. Moreover, arbitrage constraint and stock characteristics exert a joint moderating effect on sentiment reversal. Less arbitrage constraint can strengthen sentiment reversal only when the market for individual stocks is dominated by noise traders. Based on the results obtained, we discuss asset pricing, liquidity management, and market intervention.

How are sentiments on autonomous vehicles influenced? An analysis using Twitter feeds

Public opinion on autonomous vehicles (AVs) is an important topic as AVs are expected to change the transportation system dramatically. Although the topic has been discussed extensively by many researchers and experts, it is beneficial to complement current studies by explaining the AV-related sentiment variations leveraging the unique social media features.Different from traditional survey-based studies, this study relies on tweets to understand the sentiments on AVs. A comprehensive model framework is proposed to categorize sentiments, recognize critical dates with sentiment changes, and explain sentiment variations. The results indicate that the general sentiment is positive towards AVs, but social media users have sentiment biases towards different AV terms. Significant sentiment changes are often linked with major social events related to AVs, and the general public is more sensitive to social events than the most active users.A wide range of policy insights are discussed based on the results of the analyses, including policies related to safety, pricing, unemployment, and AV adoption rate. Possible challenges and the corresponding strategies are also discussed. These insights will be helpful for the public agencies, automobile manufacturers, and technology companies in gaining a better understanding of AV adoption, and in preparing for the future of transportation systems.

Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics.

There is a compelling and pressing need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of social media data from early February and late March 2021 shows that, despite the overall strength of positive sentiment and despite the increasing numbers of Americans being fully vaccinated, negative sentiment towards COVID-19 vaccines still persists among segments of people who are hesitant towards the vaccine. In this study, we perform sentiment analytics on vaccine tweets, monitor changes in public sentiment over time, contrast vaccination sentiment scores with actual vaccination data from the US CDC and the Household Pulse Survey (HPS), explore the influence of maturity of Twitter user-accounts and generate geographic mapping of tweet sentiments. We observe that fear sentiment remained unchanged in populous states, whereas trust sentiment declined slightly in these same states. Changes in sentiments were more notable among less populous states in the central sections of the US. Furthermore, we leverage the emotion polarity based Public Sentiment Scenarios (PSS) framework, which was developed for COVID-19 sentiment analytics, to systematically posit implications for public policy processes with the aim of improving the positioning, messaging, and administration of vaccines. These insights are expected to contribute to policies that can expedite the vaccination program and move the nation closer to the cherished herd immunity goal.

Social Media Behavior and Emotional Evolution during Emergency Events.

Online social networks have recently become a vital source for emergency event news and the consequent venting of emotions. However, knowledge on what drives user emotion and behavioral responses to emergency event developments are still limited. Therefore, unlike previous studies that have only explored trending themes and public sentiment in social media, this study sought to develop a holistic framework to assess the impact of emergency developments on emotions and behavior by exploring the evolution of trending themes and public sentiments in social media posts as a focal event developed. By examining the event timelines and the associated hashtags on the popular Chinese social media site Sina-Weibo, the 2019 Wuxi viaduct collapse accident was taken as the research object and the event timeline and the Sina-Weibo tagging function focused on to analyze the behaviors and emotional changes in the social media users and elucidate the correlations. It can conclude that: (i) There were some social media rules being adhered to and that new focused news from the same event impacted user behavior and the popularity of previous thematic discussions. (ii) While the most critical function for users appeared to express their emotions, the user foci changed when recent focus news emerged. (iii) As the news of the collapse deepened, the change in user sentiment was found to be positively correlated with the information released by personal-authentication accounts. This research provides a new perspective on the extraction of information from social media platforms in emergencies and social-emotional transmission rules.

Data analysis of resident engagement and sentiments in social media enables better household waste segregation and recycling

Waste segregation, recycling and reduction have been prioritised in the Circular Economy transition of household waste management to reduce environmental impacts. With digitalisation and innovation developments in waste management, residents become more active on waste management-related social media platforms. However, there is still needed a tangible analysis of resident engagement (e.g. user comments and interactions) and related sentiment changes on such platforms to enhance waste management and ease the environmental burden at source. This study develops an integrated solution to analyse resident engagement by leveraging statistical analysis and text-mining methods. Four interrelated components are incorporated in the solution: population behaviour quantification, sentiment analysis and dynamics, popular concerns and probability distribution fitting, and rule-based managerial insight identification. The novel solution is applied to a real-world case study on a subscription account related to waste management in Shanghai. This research produces several major observations based on the studied case: (i) The resident engagement Monday-to-Thursday was more active than Friday-to-Sunday. (ii) Compared to 2018, the resident engagement by commenting on online posts was elevated by 107.1% in 2019 when Shanghai introduced a new management policy. Meanwhile, the yearly resource-type waste collection was increased by 114.5% in 2019. (iii) It took approximately one year to recover positive sentiments in user comments after introducing the policy. However, the comments with negative sentiments assisted in improving waste management. (iv) The best-fitted negative binomial distribution of the number of votes for user comments could guarantee the managerial insight identification from the minority of comments with popular concerns.

Making Space for Social and Emotional Learning in Science Education

Social-emotional learning (SEL) is known to improve student outcomes but is rarely combined with STEM. In this paper we present an action research study to examine the impact of a STEM + SEL curriculum intervention to address a real-world school conflict. One hundred sixth–eighth graders and four teachers participated in an in-person facilitation of a SEL Arthropod curriculum, DIFFERENT. After the intervention, students completed open-ended couplet statements about arthropod behavior, tarantulas, and humans designed to measure sentiment change. Answers were manually coded using inductive coding on a scale of negative (1) to positive (5). Statement sentiments significantly shifted from negative to neutral and negative to positive for all three questions. Neutral to positive shifts were only significant for the couplet statements about arthropod behavior. This study reports the first confirmed instance of successful use of arthropods for SEL within a curriculum that integrates students’ social-emotional skills within a science classroom.

Long-term Effects of the COVID-19 Pandemic on Public Sentiments in Mainland China: Sentiment Analysis of Social Media Posts.

BackgroundThe COVID-19 outbreak has induced negative emotions among people. These emotions are expressed by the public on social media and are rapidly spread across the internet, which could cause high levels of panic among the public. Understanding the changes in public sentiment on social media during the pandemic can provide valuable information for developing appropriate policies to reduce the negative impact of the pandemic on the public. Previous studies have consistently shown that the COVID-19 outbreak has had a devastating negative impact on public sentiment. However, it remains unclear whether there has been a variation in the public sentiment during the recovery phase of the pandemic.ObjectiveIn this study, we aim to determine the impact of the COVID-19 pandemic in mainland China by continuously tracking public sentiment on social media throughout 2020.MethodsWe collected 64,723,242 posts from Sina Weibo, China’s largest social media platform, and conducted a sentiment analysis based on natural language processing to analyze the emotions reflected in these posts.ResultsWe found that the COVID-19 pandemic not only affected public sentiment on social media during the initial outbreak but also induced long-term negative effects even in the recovery period. These long-term negative effects were no longer correlated with the number of new confirmed COVID-19 cases both locally and nationwide during the recovery period, and they were not attributed to the postpandemic economic recession.ConclusionsThe COVID-19 pandemic induced long-term negative effects on public sentiment in mainland China even as the country recovered from the pandemic. Our study findings remind public health and government administrators of the need to pay attention to public mental health even once the pandemic has concluded.

Using E-GARCH to Analyze the Impact of Investor Sentiment on Stock Returns Near Stock Market Crashes.

Purpose: Investor sentiment, the willingness of market participants to invest, is a difficult concept to measure. Exploring the relationship between investor sentiment and stock returns can reveal how investor sentiment affects the operation of the stock market. Such an understanding can assist market participants in making more rational investment decisions based on market laws. Such an understanding can also assist regulators in their roles of supervision and policy making.Methodology: Although the E-GARCH model has the advantage of considering volatility clustering, it has not previously been used to investigate the impact of investor sentiment changes on the Shanghai Composite Index's market return. This research therefore applies the E-GARCH approach to data from 2015 to 2018, to explore the influence of investor sentiment on the return rate of the Shanghai Composite Index.Main Findings: There are three main findings. First, when the investor sentiment is increased by the same amount, the rate of return before a stock market crash will have a smaller increase than the rate of change after the crash, which is a new finding. Second, the rate of return on stocks is susceptible to emotional sentiment, rather than simply depending on stock price. Third, the tendency of retail investors to follow the crowd is less in periods of pessimism than it is in periods of optimism, which, in turn, can push up stock yields.Application: Based on these research results, this article can provide insights to understand how investors' subjective judgments on future earnings affect their investment behavior and how great the impact is on the market. At the same time, it can help investors make more rational investment decisions based on an understanding of market laws, and help regulators with guidance for their supervision and policy making.Originality/Value: This paper contributes to the theory of the investor sentiment index, improving the index construction method by adding two sentiment proxy indicators: investor activity ACT and stock market leverage level. After constructing the sentiment index and comparing it with the stock market index (Shanghai Composite Index), the fit is found to be improved.

ANALYSIS OF CUSTOMER SENTIMENT ON PRODUCT FEATURES AFTER THE OUTBREAK OF CORONAVIRUS DISEASE (COVID-19) BASED ON ONLINE REVIEWS

AbstractThe outbreak of the coronavirus disease not only caused many deaths worldwide but also severely affected the development of the global economy, such as supply chain disruptions, plummeted demand, unemployment, etc. These social changes have led to changes in customers' purchasing patterns. Therefore, it is more important than ever for manufacturers to quickly identify and respond to changing customer purchasing patterns and requirements. However, few studies have been done on dynamic changes in customer preferences for product features following COVID-19 spread. This study aims to investigate the dynamic change of customer sentiment on product features following COVID-19 through sentiment analysis based on online reviews. The proposed methodology consists of two main processes: feature extraction and sentiment analysis. After finding a specific feature of the product through feature extraction, the words used to mention the feature in the review were analyzed for sentiment analysis of customers. To demonstrate the methodology, a case study is conducted using new and refurbished smartphone reviews to investigate the dynamic changes in customer sentiment during COVID-19.

Can changes in sentiments influence consumer behavior? Evidence from the Trump‐Russia investigation

AbstractWe examine the hypothesis that animal spirits might affect economic behavior by analyzing how consumer spending changes in response to the news coverage of the investigation into coordination between Donald Trump's presidential campaign and Russia. Using daily consumption data, we find that Republicans and Democrats responded differently to the news. Increased media coverage led households in counties that voted for Hillary Clinton to spend less: On the days of heaviest news coverage, spending declined by $0.007 to $0.011 per household member for every point added to Clinton's vote share. There is evidence that in Republican counties, the coverage had opposite effects.

The Causal Nexus of Consumer and Business Confidence Indexes in Early Pandemic Period: Evidence from OECD Countries

The COVID-19 pandemic has been shown dire consequences for the global economy, not only in the past and present but also in the future. These consequences are not only humanitarian but also financial and economic. This article raises the question of whether the state of the health system is a factor that determines the direction of changes in consumer and business sentiment during the COVID-19 or whether other factors are more significant. The goal is to find out whether there is real progress in the national health system of a particular country or a regression and on this base to answer the question: What is more important for the expectations of the population and industry during the spread of the pandemic; the dynamics of the development of the health system or other factors? To assess the dynamics of the development of the health care system in different countries, we used the annual data on individual health indicators of the OECD countries for 2006–2019. There were identified countries with dynamic development and a slowing/deteriorating health system. Based on Granger’s approach in EViews, we used the Augmented Dickey–Fuller test and admit that health care systems are not a determining factor in consumer and business sentiment during a pandemic, i.e., only economic factors. The research contributes to the developed COVID-19 research by examining the impact of the changes in the mutual influence of Confidence indexes and macro indicators during the pandemic.

COVID-19 Vaccine-Related Discussion on Twitter: Topic Modeling and Sentiment Analysis.

BackgroundVaccination is a cornerstone of the prevention of communicable infectious diseases; however, vaccines have traditionally met with public fear and hesitancy, and COVID-19 vaccines are no exception. Social media use has been demonstrated to play a role in the low acceptance of vaccines.ObjectiveThe aim of this study is to identify the topics and sentiments in the public COVID-19 vaccine–related discussion on social media and discern the salient changes in topics and sentiments over time to better understand the public perceptions, concerns, and emotions that may influence the achievement of herd immunity goals.MethodsTweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, the day the World Health Organization declared COVID-19 a pandemic, to January 31, 2021. We used R software to clean the tweets and retain tweets that contained the keywords vaccination, vaccinations, vaccine, vaccines, immunization, vaccinate, and vaccinated. The final data set included in the analysis consisted of 1,499,421 unique tweets from 583,499 different users. We used R to perform latent Dirichlet allocation for topic modeling as well as sentiment and emotion analysis using the National Research Council of Canada Emotion Lexicon.ResultsTopic modeling of tweets related to COVID-19 vaccines yielded 16 topics, which were grouped into 5 overarching themes. Opinions about vaccination (227,840/1,499,421 tweets, 15.2%) was the most tweeted topic and remained a highly discussed topic during the majority of the period of our examination. Vaccine progress around the world became the most discussed topic around August 11, 2020, when Russia approved the world’s first COVID-19 vaccine. With the advancement of vaccine administration, the topic of instruction on getting vaccines gradually became more salient and became the most discussed topic after the first week of January 2021. Weekly mean sentiment scores showed that despite fluctuations, the sentiment was increasingly positive in general. Emotion analysis further showed that trust was the most predominant emotion, followed by anticipation, fear, sadness, etc. The trust emotion reached its peak on November 9, 2020, when Pfizer announced that its vaccine is 90% effective.ConclusionsPublic COVID-19 vaccine–related discussion on Twitter was largely driven by major events about COVID-19 vaccines and mirrored the active news topics in mainstream media. The discussion also demonstrated a global perspective. The increasingly positive sentiment around COVID-19 vaccines and the dominant emotion of trust shown in the social media discussion may imply higher acceptance of COVID-19 vaccines compared with previous vaccines.

Investor sentiment, realized volatility and stock returns

PurposeThis paper examines the relationship between volatility, sentiment and returns in terms of levels and changes for both lower and higher data frequencies using quantile regression (QR) method.Design/methodology/approachIn the first step, the study applies the Granger causality test to understand the causal relationship between realized volatility, returns and sentiment as levels and changes. In the second step, the study employs a QR method to investigate whether investor sentiment and returns can predict realized volatility. This regression method gives robust results irrespective of distributional assumptions and to outliers in the dependent variable.FindingsEmpirical results show that the VIX volatility index is a better fear gauge of market-wide investors' sentiments and has a predictive power for future realized volatility in terms of levels and changes for both higher and lower data frequencies. This study provides evidence that the relationship between realized volatility, investor sentiment and returns, respectively, is not symmetric for all quantiles of QR, as opposed to OLS regression. Furthermore, this work supports the behavioral theory beyond leverage hypothesis in explaining the asymmetric relation between returns and volatility at higher and lower data frequencies.Originality/valueThis paper adds to the limited understanding of investor sentiment’s impact on volatility by proposing a QR model which provides a more complete picture of the relationship at all parts of the volatility distribution for both higher and lower data frequencies and in terms of levels and changes. To the author knowledge, this is the first paper to study the volatility responses to positive and negative sentiment changes for developed market and to use both lower and higher data frequencies as well as data in terms of levels and changes.

Text Mining Approaches to Analyze Public Sentiment Changes Regarding COVID-19 Vaccines on Social Media in Korea.

The COVID-19 pandemic has affected the entire world, resulting in a tremendous change to people’s lifestyles. We investigated the Korean public response to COVID-19 vaccines on social media from 23 February 2021 to 22 March 2021. We collected tweets related to COVID-19 vaccines using the Korean words for “coronavirus” and “vaccines” as keywords. A topic analysis was performed to interpret and classify the tweets, and a sentiment analysis was conducted to analyze public emotions displayed within the retrieved tweets. Out of a total of 13,414 tweets, 3509 were analyzed after preprocessing. Eight topics were extracted using the Latent Dirichlet Allocation model, and the most frequently tweeted topic was vaccine hesitation, consisting of fear, flu, safety of vaccination, time course, and degree of symptoms. The sentiment analysis revealed a similar ratio of positive and negative tweets immediately before and after the commencement of vaccinations, but negative tweets were prominent after the increase in the number of confirmed COVID-19 cases. The public’s anticipation, disappointment, and fear regarding vaccinations are considered to be reflected in the tweets. However, long-term trend analysis will be needed in the future.

Leveraging Twitter data to understand public sentiment for the COVID‐19 outbreak in Singapore

Microblogging has become one of the most useful tools for sharing everyday life events and news and for expressing opinions about those events. As Twitter posts are short and constantly being generated, they are a great source for providing public sentiment towards events that occurred throughout the COVID-19 period in Singapore. In this project, we perform sentiment analysis and topic modeling on the tweets about COVID-19 in Singapore, from 1 February 2020 to 31 August 2020. We accomplished this by collecting tweets discussing about COVID-19 and geolocated as ‘Singapore’, using the Python library ‘SNSCRAPE’. We used the sentiments returned from the VADER lexicon-based classifier and emotions from pre-trained recurrent neural networks to find correlations between real-life events and sentiment changes throughout the whole period. From our analysis, we discovered an increase in tweets about COVID-19 during key periods such as the circuit breaker and found that the overall sentiment polarity was dominantly positive. However, emotion analysis revealed that there were changes in the prevalence of fear and joy emotions over time, due to real-life COVID-19 developments in Singapore. Additionally, sentiment polarity was found to differ from topic to topic.