Public Emotions Research Articles

Exploring Public Emotions on Obesity During the COVID-19 Pandemic Using Sentiment Analysis and Topic Modeling: Cross-Sectional Study.

Obesity is a chronic, multifactorial, and relapsing disease, affecting people of all ages worldwide, and is directly related to multiple complications. Understanding public attitudes and perceptions toward obesity is essential for developing effective health policies, prevention strategies, and treatment approaches. This study investigated the sentiments of the general public, celebrities, and important organizations regarding obesity using social media data, specifically from Twitter (subsequently rebranded as X). The study analyzes a dataset of 53,414 tweets related to obesity posted on Twitter during the COVID-19 pandemic, from April 2019 to December 2022. Sentiment analysis was performed using the XLM-RoBERTa-base model, and topic modeling was conducted using the BERTopic library. The analysis revealed that tweets regarding obesity were predominantly negative. Spikes in Twitter activity correlated with significant political events, such as the exchange of obesity-related comments between US politicians and criticism of the United Kingdom's obesity campaign. Topic modeling identified 243 clusters representing various obesity-related topics, such as childhood obesity; the US President's obesity struggle; COVID-19 vaccinations; the UK government's obesity campaign; body shaming; racism and high obesity rates among Black American people; smoking, substance abuse, and alcohol consumption among people with obesity; environmental risk factors; and surgical treatments. Twitter serves as a valuable source for understanding obesity-related sentiments and attitudes among the public, celebrities, and influential organizations. Sentiments regarding obesity were predominantly negative. Negative portrayals of obesity by influential politicians and celebrities were shown to contribute to negative public sentiments, which can have adverse effects on public health. It is essential for public figures to be mindful of their impact on public opinion and the potential consequences of their statements.

Evolution and spatiotemporal analysis of earthquake public opinion based on social media data

As critical conduits for the dissemination of online public opinion, social media platforms offer a timely and effective means for managing emergencies during major disasters, such as earthquakes. This study focuses on the analysis of online public opinions following the Maduo M7.4 earthquake in Qinghai Province and the Yangbi M6.4 earthquake in Yunnan Province. By collecting, cleaning, and organizing post-earthquake Sina Weibo (short for Weibo) data, we employed the Latent Dirichlet Allocation (LDA) model to extract information pertinent to public opinion on these earthquakes. This analysis included a comparison of the nature and temporal evolution of online public opinions related to both events. An emotion analysis, utilizing an emotion dictionary, categorized the emotional content of post-earthquake Weibo posts, facilitating a comparative study of the characteristics and temporal trends of online public emotions following the earthquakes. The findings were visualized using Geographic Information System (GIS) techniques. The analysis revealed certain commonalities in online public opinion following both earthquakes. Notably, the peak of online engagement occurred within the first 24 hours post-earthquake, with a rapid decline observed between 24 to 48 hours thereafter. The variation in popularity of online public opinion was linked to aftershock occurrences. Adjusted for population factors, online engagement in areas surrounding the earthquake sites and in Sichuan Province was significantly high. Initially dominated by feelings of “fear” and “surprise”, the public sentiment shifted towards a more positive outlook with the onset of rescue operations. However, distinctions in the online public response to each earthquake were also noted. Following the Yangbi earthquake, Yunnan Province reported the highest number of Weibo posts nationwide; in contrast, Qinghai Province ranked third post-Maduo earthquake, attributable to its smaller population size and extensive damage to communication infrastructure. This research offers a methodological approach for the analysis of online public opinion related to earthquakes, providing insights for the enhancement of post-disaster emergency management and public mental health support.

How to Make Flower Borders Benefit Public Emotional Health in Urban Green Space: A Perspective of Color Characteristics

The emotional health benefits of urban green space have been widely recognized. Flower borders, as a perennial plant landscape, have gradually become a current form of plant application in urban green spaces due to their rich color configurations. However, the related research primarily focuses on the impact of urban green spaces on public health, with relatively little attention given to how the colors of flower borders affect public emotional health. This study explored the relationship between the flower borders color characteristics and the public emotional health. In this study, 24 sample images were used as experimental materials, which selected based on their color richness and harmony. Additionally, face recognition technology and online random questionnaires were utilized to measure the public basic emotions and pleasure, respectively. The result shows that, based on the HSV color model and expert recommendations, 19 color characteristics were identified. The correlation analysis of the results from the public emotion with these color characteristics revealed that 13 color characteristics correlated with public emotional pleasure. Among them, blue, neutral purple, and low saturation were positively correlated. Through factor analysis, these thirteen color characteristics were summarized and categorized into four common factors (F1–F4), three of which are related to color. They are “low saturation of blue-violet percentage” (F1), “color configuration diversity” (F2), “bright red percentage” (F3), and “base green percentage” (F4), with F1 having the largest variance explained (27.88%). Finally, an evaluation model of color characteristics was constructed based on the variance explained by these four factors, which was demonstrated to effectively predict the level of public emotional pleasure when viewing flower borders. The results shed light on the effects of color characteristics on public emotions and provide new perspectives for subsequent flower border evaluations. Our results provide a valuable reference for future flower border color design, aiming to better improve public emotional health.

Empathy in public relations practice of South Korea

Using a qualitative approach, this study explores the role of empathy as a critical skill that can improve public relations practice. Specifically, it investigates how South Korean public relations practitioners perceive the role of empathy based on their life and work experiences, beliefs, and feelings. This theme is considered within the context of the Korean culture of Shim-Cheong, a state focused on “we-ness,” and Cheong. The study results reveal three important aspects of empathy for public relations practitioners: 1) an empathic appraisal that allows evaluation of public emotions, 2) a sensitivity that enables detection of/predicts a change in public mood, and 3) sympathetic arousal that ensures sincere communication of empathy in a crisis. When the public perceives organizations and their issues as problematic, public affect changes from positive to negative. Empathy as capacity, intuition, and attitude allows public relations practitioners to determine when public affect has significantly changed South Koreans in the culture of Shim-Cheong. Since South Koreans tend to express positive moods more readily than negative emotions, practitioners can use an affect monitoring system to identify social trends and perform more in-depth analyses to assess contextual meanings and identify potential risks.

Embarrassment as a public vs. private emotion and symbolic coping behaviour.

In dealing with embarrassment, individuals engage in symbolic coping behaviours (e.g., hiding one's face by wearing sunglasses). Research investigated these behaviours when embarrassment is experienced as a public emotion (e.g., others present). Contrary, there is emerging evidence showing that embarrassment can be experienced as a private emotion (e.g., no others present) as well. This is why the present research seeks to enhance previous work on symbolic coping behaviours and investigates to what extent symbolic coping behaviours differ when embarrassment is experienced in public and private. First, the present study finds that individuals experience embarrassment as a private as well as a public emotion. Second, both types of embarrassment relate to symbolic coping behaviours. Third, while both types of embarrassment experience a preference for face-hiding products there are differences in symbolic coping behaviours. Fourth, the study transfers extant research to a different cultural context.

Improving responsible Media Reporting on Student Suicide to Reduce Negative Public Opinion: Text Mining Based on Sina Weibo in China.

The aims of this study were to explore whether student suicide reporting is consistent with media recommendations for suicide reporting; analyze public opinion and sentiments toward student suicide reports. A keyword search was performed on the WeiboReach platform. This study included 113 student suicide report posts and 176,262 readers' comments on suicide news reports. Hierarchical generalized linear modeling was used to analyze the relationships between adherence to reporting recommendations and negative emotions in readers' comments. None of the media reporting of student suicide was consistent with all of the media recommendations for suicide reporting. Netizens were less likely to post negative comments when the reports describe the suicide method used (OR 1.169, 95% CI 1.022∼1.337), and not specifying the cause of suicide was a protective factor for public negative emotion (OR 0.799, 95% CI 0.707, 0.905). The findings suggest improving responsible media reporting on student suicide to reduce negative public emotion.

Discovering public attitudes and emotions toward educational robots through online reviews: a comparative analysis of Weibo and Twitter

PurposePublic reviews on educational robots are of great importance for the design, development and management of the most advanced robots with an educational purpose. This study explores the public attitudes and emotions toward educational robots through online reviews on Weibo and Twitter by using text mining methods.Design/methodology/approachOur study applied topic modeling to reveal latent topics about educational robots through online reviews on Weibo and Twitter. The similarities and differences in preferences for educational robots among public on different platforms were analyzed. An enhanced sentiment classification model based on three-way decision was designed to evaluate the public emotions about educational robots.FindingsFor Weibo users, positive topics tend to the characteristics, functions and globalization of educational robots. In contrast, negative topics are professional quality, social crisis and emotion experience. For Twitter users, positive topics are education curricula, social interaction and education supporting. The negative topics are teaching ability, humanistic care and emotion experience. The proposed sentiment classification model combines the advantages of deep learning and traditional machine learning, which improves the classification performance with the help of the three-way decision. The experiments show that the performance of the proposed sentiment classification model is better than other six well-known models.Originality/valueDifferent from previous studies about attitudes analysis of educational robots, our study enriched this research field in the perspective of data-driven. Our findings also provide reliable insights and tools for the design, development and management of educational robots, which is of great significance for facilitating artificial intelligence in education.

Deep parallel contextual analysis framework based emotion prediction in community wellness communications on social media

Understanding public emotion on social media about community wellness is crucial for enhancing health awareness and guiding policy-making. In order to more fully mine the deep contextual semantical information of short texts and further enhance the effectiveness of emotion prediction in social media, we propose the Deep Parallel Contextual Analysis Framework (DPCAF) in the community wellness domain, specifically addressing the challenges of limited text length and available semantical features in social media text. Specifically, at the embedding layer, we first utilize two different word embedding techniques to generate high-quality vector representations, aiming to achieve more comprehensive semantical capture, stronger generalization ability, and more robust model performance. Subsequently, in the deep contextual layer, the obtained representations are fused with POS and locational representations, and processed through a deep parallel layer composed of Convolutional Neural Networks and Bidirectional Long Short-Term Memory Network. An attention model is then used to further extract semantical features of social media texts. Finally, these deep parallel contextual representations are post-integrated for emotion prediction. Experiments on a dataset collected from social media regarding community wellness demonstrate that compared to benchmark models, DPCAF achieves at least a 4.81 % increase in Precision, a 3.44 % increase in Recall, and a 10.81 % increase in F1-score. Relative to the most advanced models, DPCAF shows a minimum improvement of 2.65 % in Precision, 3.02 % in Recall, and 2.53 % in F1-score.

Influence of emotions on coping behaviors in crisis: a computational analysis of the COVID-19 outbreak

Widespread public crises often give rise to the proliferation of sensationalized rumors and conspiracy theories, which can evoke a variety of public emotions. Despite the growing importance of research on the relationship between emotions and coping behaviors in crisis, a dearth of natural observation-based investigation has been limiting theory development. To address this gap, this study conducted computational research to study the U.S. public’s discrete emotions and coping behaviors during the COVID-19 outbreak crisis, analyzing Twitter data, Google Trends data, and Google Community Mobility data. The results revealed that anger and fear were relatively more prominent emotions experienced by the public than other discrete emotions. Regarding the impacts of emotions on coping behaviors, it was found that the prevalence of low-certainty and retreat emotions was related to increased information-seeking and information-transmitting behaviors. Also, the prevalence of both high-certainty and low-certainty emotions during the COVID-19 outbreak was positively related to the public’s compliance with public health recommendations.

Public emotion and response immediately following the death of George Floyd: A sentiment analysis of social media comments

Social media use has become a critical form of social interaction connecting people of all ages, and across geographic regions, political, and philosophical persuasions. The results of these interactions are massive amounts of data which, through exploratory methods like sentiment analysis, can provide insight into overall public emotion and response to public and key sociopolitical events. The present study explores the sentiment surrounding the Black Lives Matter Facebook page by analyzing the comments under the video of the killing of George Floyd, posted on May 25, 2020. This examination of sentiment uses assumptions of selective exposure theory. We employed a three-pronged sentiment analysis approach and topic modeling and found an overall negative polarity in public sentiment among 7,137 Facebook comments on the video of George Floyd's death. The findings reveal a divisive response to the video which captured the death of George Floyd, which more generally suggests negative emotion in response to the Black Lives Matter movement. A sentiment test also returned a noticeably high happiness score, which might imply the widely unavoidable effects of trolls in internet discourse in the current racial context.

Public emotions and visual perception of the East Coast Park in Singapore: A deep learning method using social media data

Social media data mining has become a prevailing approach to understanding people’s emotional response to, and perception of, public spaces. Drawing on Google Maps Reviews’ text reviews and shared images, this study develops a deep learning framework that combines Transformer BERT and CNN-VGG models to explore public emotions and visual perception of the East Coast Park (ECP) in Singapore. The results show that (1) public emotions of the ECP are predominantly joy, with minor emotional fluctuations in 2020 due to the pandemic lockdown; (2) CNN-VGG image classification is an effective tool to capture people’s visual preferences of public spaces, (3) public emotions across 12 specific public spaces are predominantly joy and neutral, but the results of people’s visual preference exhibited considerable diversity, and (4) image data is mostly associated with joy or neutral emotions, generally indicating users’ satisfaction of ECP public space, and for this reason, there is a lack of sound base to identify environmental factors associated with people’s negative emotions. This study shows that the proposed deep learning framework is effective in using social media data to understand the public's emotional response to and visual preference of urban public spaces.

Exploring the non-linear relationship and synergistic effect between urban built environment and public sentiment integrating macro- and micro-level perspective: a case study in San Francisco.

Public sentiment can effectively evaluate the public's feelings of well-being in the urban environment and reflect the quality of the spatial environment to a certain extent. Previous studies on the relationship between public sentiment and urban built environmental factors have yielded meaningful results. However, few studies have focused on the effect of micro-built environment on public sentiment at the street level, which directly shapes people's perceptions. In addition, the nonlinear relationship and synergistic effect among urban built environmental factors have been commonly disregarded in previous studies, resulting in an incomplete understanding of the impact of urban built environment on public emotions. Therefore, this paper takes San Francisco as a study case to explore the complex relationship between urban built environmental factors and public emotions. Specifically, this paper measures the polarity of public emotions through sentiment analysis on Twitter data, establishes a comprehensive built environment index system from both macro- and micro- perspectives, and subsequently explores the complex relationship between the urban built environment and public sentiment through the OLS model and Shapley Additive Explanation algorithm. Results show that: (1) micro-built environmental factors have a significant influence on public emotion, although they have been frequently ignored. (2) Public sentiment tends to be more positive in areas with recreation facilities, mixed land use, rich street view visual environment, suitable thermal and acoustic environment, balanced income, and a suitable degree of high population density. (3) A nonlinear relationship and threshold effect exist between the built environmental variables and the semantic orientations of public emotion. Environment improvement strategies based on the synergic effect between variables can effectively promote the generation of positive emotions. Our empirical findings can offer valuable insights to promote feelings of well-being and foster an urban development approach through strategic interventions within the urban built environment.

Public Opinion About COVID-19 on a Microblog Platform in China: Topic Modeling and Multidimensional Sentiment Analysis of Social Media.

The COVID-19 pandemic raised wide concern from all walks of life globally. Social media platforms became an important channel for information dissemination and an effective medium for public sentiment transmission during the COVID-19 pandemic. Mining and analyzing social media text information can not only reflect the changes in public sentiment characteristics during the COVID-19 pandemic but also help the government understand the trends in public opinion and reasonably control public opinion. First, this study collected microblog comments related to the COVID-19 pandemic as a data set. Second, sentiment analysis was carried out based on the topic modeling method combining latent Dirichlet allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT). Finally, a machine learning linear regression (ML-LR) model combined with a sparse matrix was proposed to explore the evolutionary trend in public opinion on social media and verify the high accuracy of the model. The experimental results show that, in different stages, the characteristics of public emotion are different, and the overall trend is from negative to positive. The proposed method can effectively reflect the characteristics of the different times and space of public opinion. The results provide theoretical support and practical reference in response to public health and safety events.

Beyond good and evil: imagined nation in Hindi films

ABSTRACT The influence of Indian films on popular culture and the influence of dominant political ideology on Indian films are interspersed with dialectical variations. Although a binary representation of these subtleties remains elusive, while promoting ideological superstructures in society, ‘the relatively mixed, confused, incomplete, or inarticulate consciousness' is replaced with a ‘generalised system'. In practice, the tendencies governing public emotions during moments of crisis are employed as tropes in Bollywood films. However, the ontology of political underpinnings and subversive ideological possibilities that enmesh lived experiences remain smothered by an unproblematic representation of historical events. With this rationale, the paper analyses three Bollywood films Panipat (2019), Tanhaji (2020) and Samrat Prithviraj (2022). These films seem to auger the idea of Dharma and Ramrajya in the construction of a Nation catering to the populist notion of a safe and secure nation under just Hindu rule. The paper argues that these films cater to the framework of historiography promulgated by the colonial historians on one hand; and function as a contrapuntal response to the ideological moorings of the leaders of the All-India Muslim League. This objective is achieved by a stereotypical and binary projection of good and evil by glossing over the ‘mixed consciousness’.

Unraveling energy justice in NYC urban buildings through social media sentiment analysis and transformer deep learning

This study explores the intricate relationship between human sentiment on social media data, herein tweet posts on X platform, urban building characteristics, and the socio-spatial dynamics of New York City (NYC) boroughs. Leveraging Natural Language Processing (NLP) techniques, particularly sentiment analysis, augmented by the capabilities of transformer deep learning models, RoBERTa, the study places particular emphasis on the term ‘Stay-at-Home’ to encapsulate the pronounced shift in building occupancy during the pandemic's inaugural year. This focus intertwines with pivotal terms like ‘Energy Bill’ and ‘HVAC’, shedding light on their interconnected implications. The sentiment analysis leverages data from New York City's PLUTO and the Department of Energy's LEAD databases to emotional disparities connected to urban building characteristics as well as demographic and socioeconomic factors. This analytical approach unravels prevailing public emotions and extends the discussion to include energy justice concerns, viewing them through the lens of the city's built infrastructure. The research uncovers profound disparities in the built environment and the allocation of resources in NYC, highlighting the critical need to embrace a spatial justice framework for a sustainable future. This research can aid designers, planners, and policymakers in their efforts to promote equitable and inclusive urban development.

Discourse and language of war: A comparison of the linguistic and rhetorical strategies employed in Russian, United States, and Ukrainian presidential speeches

In the context of war, political speeches employ emotionally charged language in the form of linguistic devices, in an attempt to persuade the audience and appeal to their emotions. This study examines how the Russia-Ukraine War was presented by Russian President Vladimir Putin, US President Joe Biden, and Ukrainian President Volodymyr Zelenskyy. This study identifies the linguistic features as well as the rhetorical strategies employed in the three presidential speeches which reflect the power and dominance of the conflicting parties in the Russia-Ukraine war. Therefore, a critical discourse analysis of three presidential speeches on the Russia-Ukraine War was conducted. The study analyzed and compared the use of vocabulary, semantic structure, grammatical devices, and rhetorical strategies used by the three presidents to reveal the extent of language manipulation and ideologies in the context war. The study revealed the power of language in political speeches in three different narratives of the war. The findings of this study indicate that word choice, grammatical structures, and rhetorical devices were employed to evoke public emotions, to reflect power and influence beliefs, attitudes, and ideologies. The study revealed that language manipulation is clearly evident through the use of linguistic strategies such as powerful word choices, pronouns, modals, tenses etc., as well as emotional and logical appeals in the form of rhetorical strategies.

The Past Evolution, Present Challenge and Future Prospect of the Post-Truth Research

In the past few years, the phenomenon of post truth has become popular in Western countries. It refers to the fact that public emotions and personal beliefs have a greater impact on facts than the actual situation itself. The history of the post truth phenomenon can be traced back to the 1970s, but in recent years, due to the widespread use of digital and social media, the spread of fake news has become even more rampant. The phenomenon of post truth has become a new political culture, regarded as a manifestation of populist movements that resist elites. Currently, the post truth phenomenon faces some challenges. With the frequent occurrence of major public emergencies, new impetus has been provided for the popularization and complexity of post truth. After the COVID-19, people began to connect their personal situation with the health of the whole society, which further aroused the public's attention to the truth. However, studying post truth faces many challenges, such as how to explain the phenomenon of post truth, the motivation of fake news, and the persuasive strategies of news. In future prospects, the focus of truth after research may shift towards the role of emotions. Emotions are increasingly valued in social sciences. Understanding the relationship between emotions and facts will help to better understand post truth phenomena. Emotion is not only the opposite of truth or rationality, but also an important component of cognitive psychology and even a resource for social mobilization and integration. Further research on the role of emotional discourse will help reveal the social and cultural aspects of post truth phenomena. In summary, the evolution of post truth phenomena, current challenges, and future prospects are all important aspects of studying post truth. A thorough study of the history, motivation, and impact of post truth phenomena will help us better understand and respond to the challenges of post truth phenomena, and provide new directions for future research.

A Comparative Study of Chinese and American Political Speech on COVID-19 from the Perspective of Speech Act Theory

As crucial instruments of power, political speeches significantly impact individuals’ political lives. Utilizing Austin’s Speech Act theory as a theoretical framework, this study comparatively analyzes the initial speeches given by the presidents of China and the United States during the COVID-19 pandemic, aiming to uncover the manner in which both presidents convey their political attitudes through illocutionary acts and investigate the value disparities embedded within their speech acts. The results indicate that the Chinese political address related to COVID-19 includes a greater frequency of directive and representative speech, accentuating the significance of summation and guidance while mirroring China’s status as a socialist nation that prioritizes its citizens. Conversely, the American discourse features more expressive and directive language, underlining the role of speech in modulating public emotions and articulating the speaker’s personal political agenda. This research substantiates and illustrates the practicality of employing Speech Act Theory in political discourse analysis, aiming to enlighten future research within the realm of discourse investigation.

Unfolding Affective Atmospheres during the COVID-19 Pandemic Crisis: The Constitution of Affective Governance in Taiwan

This article primarily explores the generation and circulation of the affective atmosphere during the COVID-19 crisis, providing a nuanced understanding of the emergence of affective governance. It constructs an analytical framework to examine the spatiality, collectivity, and authority within affective atmospheres, which are essentially interrelated and mutually influential. It argues that the affective atmosphere can be manipulated to direct public emotions and actions, shaping specific forms of affective governance. Through a case study of Taiwan’s response to the COVID-19 pandemic, this paper unfolds how affective dynamics converge to reinforce Taiwanese nationalism through the construction of atmospheric walls, social and psychological mobilisation, and technology-based interventions. It demonstrates that governments adapt to changing atmospheres and achieve their strategic intentions through performative spatial arrangements, emotional appeals and affective rhetoric, as well as the development of a digital surveillance landscape. It illuminates how these epidemic transmission control strategies have driven a shift in governance models towards greater emphasis on transforming and manipulating affective nationalism to stimulate public sentiments and collective actions. Finally, this paper suggests that further research and discussion is needed regarding the multifaceted nature of affective atmospheres to enhance the effectiveness and efficiency of governance mechanisms, as well as to promote civic engagement and inclusivity.

The Effect of Perceived Real-Scene Environment of a River in a High-Density Urban Area on Emotions

Public sub-health has emerged as a pressing concern in densely populated urban areas. The urban environment, with its innate ability to modulate public emotions, harbors a precious resource in the form of urban rivers, which provide a serene and verdant space. This study focuses on the Liangma River in Chaoyang District, Beijing, selecting two rivers with diverse landscape features as the subjects of research. By employing physiological feedback data in conjunction with a subjective questionnaire, the emotional impact of high-density urban riverside spaces on individuals is quantitatively analyzed. Electrocardiogram (ECG) data, eye movement data, and the positive–negative emotion scale (PANAS) are subjected to data analysis. The study reveals the following key findings: (1) The riverside landscape in high-density urban areas exerts a positive influence on emotional well-being. Individuals in more natural river settings experience greater levels of contentment and relaxation, while those in areas with a higher proportion of artificial elements exhibit increased excitement and happiness. Moreover, scenes characterized by a greater degree of greening have a more pronounced soothing effect on mood. (2) A specific correlation between visual characteristics and emotional fluctuations is observed. The waterfront side of the trail exerts a stronger spatial attraction, and a higher proportion of blue and green spaces significantly contributes to stress relief. (3) The utilization of human-induced engineering technology, which captures emotional changes through physiological feedback, demonstrates a higher level of accuracy and is well-suited for small-scale studies. These findings highlight the potential of arranging diverse types of waterfront footpath landscapes in high-density urban areas and approaching waterfront landscape design and transformation from a novel perspective centered on health intervention. Such efforts hold promise for alleviating the daily pressures faced by the general public and fostering the development of a “healthy city”.