Collective Sentiment Research Articles

Online academic group sentiment analyses informing workplace online group sentiment

Purpose Researchers have investigated best practices for forming groups capable of completing projects cohesively for years. Online group formation has increased in recent years, peaking scientific interest in the sentiment that characterizes group cohesion from the point when the group is established to the stage where specific outcomes are produced. This research contributes to understanding online group dynamics by analyzing the sentiment of university students completing multiple nine-week group course projects with implications for workplaces. Design/methodology/approach Over eight nine-week terms, sentiment analyses were conducted on students' online reflection assignments, targeting their views on group interactions during group project completion. The assignment's context was to assess individual sentiment about the group experience that could build group sentiment implications for workplaces. Adult students from diverse academic and industry disciplines at a single university were participants. Four group models were considered possible drivers of student sentiment about their group experiences. Findings Punctuated Equilibrium, a classic group model, defined influences like remote distances and external obligations steering student sentiment outcomes. Instructors' active facilitation of group formation and development motivated students’ positive sentiments. Findings are akin to online organizational groups’ attempts to manage remote work and other responsibilities. Originality/value This study reinforced the importance of leveraging online students' collective sentiment to inform group dynamics in professional settings. Few studies have focused on the latter directly and exclusively. The results highlighted the sentiment kinship of online academic and organizational groups, validating the focused investigation of this study.

Sentiment Analysis on Twitter through Machine Learning: A Comprehensive Approach with User-Centric Visualisations

Sentiment analysis is a crucial field that deals with the intricate task of identifying and systematically categorizing the various perspectives and opinions expressed within the original text. In today's digital age, social media platforms serve as a prolific source of data, inundated with a relentless stream of status updates, tweets, and content imbued with sentiments. Analysing the sentiments conveyed by users in this vast reservoir of data holds a pivotal role in comprehending the collective sentiments of the user community, dissecting dialogues, and aggregating viewpoints. This, in turn, can be instrumental in shaping strategies for commerce, conducting insightful political research, and gauging the pulse of communal activities. Examining sentiments on Twitter presents an increased difficulty because of the frequency of spelling errors, casual language, icons, and emojis. This research focuses on Twitter sentiment analysis, with a specific emphasis on a particular user account. The approach involves a combination of Python programming and Machine Learning techniques. By embarking on a comprehensive sentiment analysis journey within a specific domain, the aim is to discern the profound impact of that domain's data on sentiment categorization. Furthermore, this paper introduces a novel feature that enhances the organization of a user's most recent tweets and their presentation through visual aids such as graphs, charts, and word clouds. This visualization approach empowers a more intuitive and insightful exploration of the sentiments and trends embedded within the user's Twitter activity, facilitating a deeper understanding of their thoughts and emotions as expressed through their digital interactions.

ON SATIRICAL COMMENTS: POLITICAL HUMOR OF INDONESIANS DEPICTED IN INSTAGRAM POSTS

This study explores the phenomenon of political humor within Indonesian society, focusing specifically on the expression of satirical comments through Instagram posts. Employing critical discourse analysis as the methodological framework, the study examines how Indonesian users utilize humor to comment on political events, figures, and ideologies in the digital realm. By analyzing a selected sample of Instagram posts, the study aims to shed light on the linguistic and discursive strategies employed by users to construct satirical commentary and investigate the underlying socio-political implications of such discourse. The study findings highlight the significance of satirical comments in shaping public opinion and fostering political dialogue within Indonesian society. It uncovers Instagram users' diverse strategies to express their discontent, promote alternative perspectives, and foster critical thinking through humor. The study identifies recurring themes, targets, and stylistic patterns in satirical comments, offering valuable insights into contemporary Indonesia's political discourse and collective sentiment. This study contributes to the existing literature on critical discourse analysis. It provides an in-depth analysis of the unique ways Indonesians engage in political satire through Instagram, elucidating the intricate relationship between language, humor, and political critique.

Uncovering COVID-19 conversations: Twitter insights and trends

In this paper, we delve into the public discourse surrounding COVID-19 on Twitter to unearth the collective sentiments, concerns, and spread of information during the pandemic. By leveraging a dataset of relevant tweets and corresponding ISO country codes, our analysis will map out the geographical and digital landscape of these conversations. The significance of this work lies in its potential to inform public health strategies, shape policymaking, and contribute to social research on crisis communication. Stakeholders ranging from health officials to the public have a vested interest in understanding the contours of this dialogue. Our objective is to craft a data-driven narrative through visualizations that reveal how the world engages with the pandemic on the digital front, providing actionable insights into global and local responses to COVID-19 using Machine Learning techniques.

Modeling Seasonality of Emotional Tension in Social Media

Social media has become an almost unlimited resource for studying social processes. Seasonality is a phenomenon that significantly affects many physical and mental states. Modeling collective emotional seasonal changes is a challenging task for the technical, social, and humanities sciences. This is due to the laboriousness and complexity of obtaining a sufficient amount of data, processing and evaluating them, and presenting the results. At the same time, understanding the annual dynamics of collective sentiment provides us with important insights into collective behavior, especially in various crises or disasters. In our study, we propose a scheme for identifying and evaluating signs of the seasonal rise and fall of emotional tension based on social media texts. The analysis is based on Russian-language comments in VKontakte social network communities devoted to city news and the events of a small town in the Nizhny Novgorod region, Russia. Workflow steps include a statistical method for categorizing data, exploratory analysis to identify common patterns, data aggregation for modeling seasonal changes, the identification of typical data properties through clustering, and the formulation and validation of seasonality criteria. As a result of seasonality modeling, it is shown that the calendar seasonal model corresponds to the data, and the dynamics of emotional tension correlate with the seasons. The proposed methodology is useful for a wide range of social practice issues, such as monitoring public opinion or assessing irregular shifts in mass emotions.

남북음악교류와 한반도 대중음악의 감성정치

In the wake of the 2018 Pyeongchang Winter Olympics, inter-Korean musical exchanges featuring reciprocal performances by South and North Korean performing arts troupes resumed after a hiatus of 16 years. Such exchanges began in 1985, serving as peace events affirming ethnic homogeneity and persisted intermittently, subject to interruption by conflict between the two Koreas arising from domestic or international political contexts. However, the 2018 inter-Korea performance deviated from earlier programs of gugak, traditional Korean music from the South and minjok eumak, ethnic music from the North, which had long been highlighted in previous peace events, and instead brought popular music from both sides to the forefront. This change demands closer examination and discussion.
 Since 1999, other than events primarily organized by broadcasting companies visiting North Korea, there have been no instances of the complete absence of traditional music, which represents the exclusion of national music in favor of popular music.
 This study initially examines the trajectory of South-North Korean musical exchange, then discuss the meaning of ‘ethnic’ music and the collective sentiments of popular music in the context of 21st-century Korean Peninsula Cold War politics, The allocation of meanings relation to ‘ethnicity,’ ‘nation,’ and ‘peace’ to the popular music in the nation-led South-North Korean musical exchange symbolically signifies the position of popular music in the 21st century Korean Peninsula. Consequently, the 2018 musical exchange became an event that both the elevated status of popular music, which had previously occupied a lowest position within the hierarchy of music genres in the 20th century Korean Peninsula, and effectively reflected shifts in attitudes and sentiments toward popular music.

A U-shaped relationship between the crypto fear-greed index and the price synchronicity of cryptocurrencies

In this study, we use intraday data on Bitcoin, Ethereum, Litcoin, and Monero, traded on the Bitfinex exchange, to investigate the impact of the crypto fear and greed index (FGI) on the pairwise price synchronicity of these cryptocurrencies. Based on data from February 2018 to June 2023, our results show that the relationship between investors’ collective sentiment (the FGI) and price synchronicity (as measured by the realized correlation) exhibits a U-shaped pattern rather than a linear one. This is the first study to document a U-shaped relationship between FGI-based online investor sentiment and the price synchronicity of cryptocurrencies.

Greenspace exposure is conducive to the resilience of public sentiment during the COVID-19 pandemic.

The coronavirus disease 2019 (COVID-19) pandemic significantly impacts people's sentiment and mental health, threatening their health and lives. We gathered 4.17 million geotagged social media posts from Weibo and scrutinized the nuances of the collective sentiments of netizens in four megacities in China during the first pandemic wave (from 1 December 2019 to 30 April 2020). Our findings suggest that the COVID-19 outbreak significantly reduced the Sentiment Index (SI) in China's cities, and the collective sentiments expressed in Wuhan were even more negative than those in the other three megacities. We explored the uncharted impacts of exposure to three geographical environment factors (GEFs) on SIs. Public exposure to greenspaces increased, while exposure to indoor built spaces decreased during the lockdown period. The exposure to sidewalks increased in rural areas but decreased in the main urban areas. The contributions of various GEFs to the SIs were the lowest during the lockdown period, and SIs were strongly affected by the pandemic. However, greenspace had the most potent effect on SIs, improving public sentiment resilience and mitigating mental health risks.

Soft Power in Northeast Asia, Using AI in Information Warfare

Purpose: Conflicts between Korea, the United States, Japan, North Korea, China, and Russia continue along with the competition for supremacy between the United States and China, and conflicts between South and North Korea and between China and Taiwan continue as the Korean Peninsula is an area with a high possibility of military conflict. However, even in North Korea, China, and Russia, which are closed countries, the influence of the soft power of cultural content such as dramas through the Internet is bringing about changes in collective sentiment even in closed countries. Due to these phenomena, Northeast Asia, which has been confronted with military power, is facing a new phase, and we wanted to discuss the use of AI technology in information warfare by the soft power of the Intelligence Agency.
 Method: For the expansion of AI use and research on information warfare, the historical cases of Northeast Asia were analyzed, and the evolution of literature and media was reviewed to understand the phenomenon of artificial intelligence (AI) after the 4th industrial revolution, and the themes were selected. In addition, related data were collected and reviewed, and an attempt was made to theoretically establish the research results academically.
 Results: 1. Northeast Asia is undergoing a transition from order based on hard power through military power to soft power based on cultural content. Just as the spread of culture has expanded faster and deeper the more it is controlled by the state, many researchers in Northeast Asia sympathize with the collapse of the system when asked how long such surveillance and control by dictatorships such as China and North Korea will be possible. According to this phenomenon, the influence of the power of culture on society was analyzed. 2. Artificial intelligence (AI) learning information will adversely affect sound soft power due to manipulated information and biased algorithm learning data. Due to this loophole, the Intelligence Agency will launch an information war using artificial intelligence (AI) technology that suits its own interests. In addition, the Internet will accelerate the propagation speed of distorted soft power and penetrate deeply into human life. Therefore, the Intelligence Agency is expected to analyze the influence of this distorted soft power on its country and start blocking and defending against attacks.
 Conclusion: 1. The legal system before the advent of AI is expected to be modified or supplemented by more than 50% after the advent of AI. In accordance with this paradigm shift, the authority of the Intelligence Agency in the information warfare of AI was divided into the right to investigate, the right to investigate, and the right to operate. 2. In response to the threat of using artificial intelligence (AI) in information warfare, the government of the country not only expanded the size of the Intelligence Agency but also proposed a hybrid structure of cooperation with the private sector, that is, a model of ‘hybrid defense’. Lastly, in 1983, when tensions between the US and the Soviet Union were in the Cold War, the state-of-the-art scientific equipment, a satellite for detecting nuclear missiles, recognized the US ICBM launch warning. In response, Lieutenant Colonel Stanislav Petrov, commander of the Watch Command of the Soviet Air Defense Force, determined through human intuition that this was a computer error. It reexamined the case of preventing World War III by judging computer errors through human intuition, not judgment of scientific equipment, and suggested ethical issues in information warfare.

"Conducted Properly, Published Incorrectly": The Evolving Status of Gel Electrophoresis Images Along Instrumental Transformations in Times of Reproducibility Crisis.

For the last ten years, within molecular life sciences, the reproducibility crisis discourse has been embodied as a crisis of trust in scientific images. Beyond the contentious perception of "questionable research practices" associated with a digital turn in the production of images, this paper highlights the transformations of gel electrophoresis as a family of experimental techniques. Our aim is to analyze the evolving epistemic status of generated images and its connection with a crisis of trust in images within that field. From the 1980s to the 2000s, we identify two key innovations (precast gels and gel docs) leading to a "two-tiered" gel electrophoresis with different standardization procedures, different epistemic statuses of the produced images and different ways of generating (dis)trust in images. The first tier, exemplified by differential gel electrophoresis (DIGE), is characterized by specialized devices processing images as quantitative data. The second tier, exemplified by polyacrylamide gel electrophoresis (PAGE), is described as a routine technique making use of image as qualitative "virtual witnessing." The difference between these two tiers is particularly apparent in the ways images are processed, even though both tiers involve image digitization. Our account thus highlights different views on reproducibility within the two tiers. Comparability of images is insisted upon in the first tier while traceability is expected in the second tier. It is striking that these differences occur not only within the same scientific field, but even within the same family of experimental techniques. In the second tier, digitization entails distrust, whereas it implies a collective sentiment of trust in the first tier.

SENTIMENTS OF TEACHERS IN THE IMPLEMENTATION OF CHILD PROTECTION POLICY: A PHENOMENOLOGICAL STUDY

This study aimed to explore the sentiments of teachers in the implementation of child protection policy. The researcher used a phenomenological approach method in qualitative research employing 12 teachers from one of the public schools in Davao City, of which six of them were identified for an In-Depth Interview and six for a Focus Group Discussion, with the aid of a semi-structured interview guide. The findings of the thematic analysis of the interview transcription revealed that teachers lack a deep understanding of the policy; there is a tendency for students to abuse the policy; and teachers demand for policy amendments. In addressing such sentiments, it was noted that teachers should observe proper monitoring and coordination, embrace effective strategy to address possible students abuse of the policy, and address teachers collective sentiments. It was then learned that the insights of teachers in the implementation of child protection policy include being accountable when difficult situations occur; realizing the importance of sharing good values with students, realizing the importance of establishing positive relationships with students and parents; and leading a child centered and policy-based institution. Overall, it is recommended that child protection policies be revisited with the support of DepEd to make recommendations for possible inclusions of protection and rights of teachers to due process in the policy and that schools are encouraged to participate in policy decision-making and implementation and building positive relationships with parents and learners. KEYWORDS: Child protection policy, positive relationships, rights of teachers, decision-making, monitoring and coordination

The Impact of the Social Mood on the Italian Sovereign Debt Market: A Twitter Perspective

By analysing the relationship between a new experimental daily index based on Twitter data (the Istat’s Social Mood on Economy Index—SMoEI) and the structure of Italian (and Spanish) sovereign interest rates, our work sheds new light on the great significance of the interconnections between economic sentiment and the Italian sovereign bond market. A placebo test performed on Spain introduces a possible extension of this linkage to the European market, highlighting the deep integration of financial markets within the European Monetary Union. Within a VAR and VECM framework with daily frequency data (2016–2022), we show that public shaping mechanisms play a role in the cost of debt financing. Our analysis emphasises the importance of economic sentiment when it comes to financial markets, putting the role of macroeconomic fundamentals in a different light. This result should be interpreted with caution, as updates to fundamentals can be affected by a time lag. In any case, recognising the importance of this index has at least two implications: on the one side, the SMoEI could represent a more responsive indicator for predicting investor sentiment; on the other side, media channels—as well as the European and national institutions—should gain relevance for their potential impact on collective sentiment, encouraging the importance of economic education and non-alarmist communication.

Empowering Sentiment Analysis For Improved Fashion Choices

The world of fashion is dynamic, ever-changing, and deeply influenced by individual preferences and collective sentiment. Making confident and informed fashion choices can be a daunting task, especially in an era marked by rapid trends and diverse styles. The fashion industry is ever-evolving, with trends and styles constantly changing. Making informed fashion choices that align with personal preferences and public sentiment can be challenging. "Empowering Sentiment Analysis for improved fashion choices" presents a novel approach to address this challenge. This abstract introduces a comprehensive framework that harnesses the power of sentiment analysis, a sophisticated natural language processing technique, to provide consumers and fashion enthusiasts with invaluable insights into the realm of fashion. Sentiment analysis, commonly used in understanding public opinions and emotional tones in textual data, is adapted here to decode the fashion landscape. By analyzing textual data from fashion reviews, social media posts, and comments, sentiment analysis can discern public sentiment and opinions about specific clothing items, styles, and trends. The primary objective of this initiative is to empower individuals to make better, more informed fashion choices. Through sentiment analysis, individuals can access an in-depth understanding of the prevailing sentiments and opinions surrounding specific clothing items, styles, and trends. This knowledge equips them with the tools to align their choices with current trends, explore niche styles, or even express their uniqueness confidently. By leveraging the power of machine learning models like Logistic Regression, Naïve Bayes, Support Vector Machines, Random Forest , Ada Boosting and Deep Learning algorithms the sentiment classification models are built. Furthermore, this technology fosters inclusivity and diversity in fashion decision-making by highlighting a wide range of sentiments and opinions. It acknowledges that fashion is a highly personal and subjective domain and helps individuals discover styles that resonate with their unique tastes and values.

Using Social Media & Sentiment Analysis to Make Investment Decisions

Making investment decisions by utilizing sentiment data from social media (SM) is starting to become a more tangible concept. There has been a broad investigation into this field of study over the last decade, and many of the findings have promising results. However, there is still an opportunity for continued research, firstly, in finding the most effective way to obtain relevant sentiment data from SM, then building a system to measure the sentiment, and finally visualizing it to help users make investment decisions. Furthermore, much of the existing work fails to factor SM metrics into the sentiment score effectively. This paper presents a novel prototype as a contribution to the field of study. In our work, a detailed overview of the topic is given in the form of a literature and technical review. Next, a prototype is designed and developed using the findings from the previous analysis. On top of that, a novel approach to factor SM metrics into the sentiment score is presented, with the goal of measuring the collective sentiment of the data effectively. To test the proposed approach, we only used popular stocks from the S&P500 to ensure large volumes of SM sentiment was available, adding further insight into findings, which we then discuss in our evaluation.

Advancements in Surgical Performance Evaluation and Feedback.

How today's urology trainees acquire surgical skills has changed dramatically due to multiple forces placing strain on the graduate medical education mission. The development of workplace-based assessments that deliver feedback while capturing performance data has led to a paradigm shift toward individualized learning. Delivering feedback that drives surgical skill development requires the educator to provide a meaningful assessment of the learner after an operative experience. Workplace-based assessment involves direct observation of routine clinical practice and has become a central component of competency-based medical education. Urology has the chance to fully embrace competency-based medical education, employing robust feedback mechanisms and workplace-based assessments. We must first define what it means to be a proficient urologist and design an assessment system that captures this collective sentiment. This can only be done through effective engagement and collaboration with stakeholders across our specialty.

Correlation of Narrative Evaluations to Clerkship Grades Using Statistical Sentiment Analysis.

Narrative evaluations are essential components of medical student assessment. This study evaluated how well narrative clerkship evaluation word choice correlated with an assigned letter grade. One hundred clerkship evaluations, 50 from family medicine (FM) and 50 from internal medicine (IM), with even distribution of "Honors" and "Near-Honors" among medical students that graduated in 2020 from the Oregon Health and Science University (OHSU) were examined. A textual sentiment analysis, which evaluates positive and negative word choice, was used to determine each evaluation's collective sentiment. An average sentiment score and character count were calculated for Honors and Near-Honors evaluations from both clerkship disciplines. Sentiment word totals were used to form "word clouds" that highlight the most frequent word selections. While sentiment scores positively correlated with the assigned grade, there was no statistically significant difference between the average sentiment scores among Honors and Near Honors graded evaluations within the FM or IM clerkship evaluation sets. There was no significant difference in evaluation character length among the assigned grades. Among FM evaluations, "outstanding" and "excellent" were the two most common sentiment words used in both Honors and Near-Honors. Among IM evaluations, outstanding and excellent were most commonly used in Honors evaluations, while "excellent" and "good" were most common in Near-Honors. This study outlines a novel text analysis method for analyzing narrative evaluation association with assigned grade that other institutions can utilize. Sentiment word choices are not significantly different among Honors and Near Honors clerkship narrative evaluations. The online version contains supplementary material available at 10.1007/s40670-022-01654-2.

An affect-based approach to detect collective sentiments of film audience: Analyzing emotions and attentions

Affective computing plays an important role in simulating human affects through multimedia stimuli. To provide appropriate responses and quantify emotions, it is essential to identify and interpret emotions accurately. Often the audience is influenced by media content and it has an impact on the audience's emotions. The authors aim to develop a machine vision based smart affective system that investigates the correlation between the basic facial expressions and corresponding ratings given by the audience. For the same, a notion of emotion vector is introduced to measure the elicited emotion. The idea of measuring attention is also suggested to find if the con- tent could keep the audience engaged. Our results reveal that such systems are useful in capturing and analyzing individual and collective sentiment.

World Environment Day: Understanding Environmental Programs Impact on Society Using Twitter Data Mining

Environmental sustainability awareness has encouraged the promotion of a number of environmental programs and initiatives and, accordingly, the use of social networks for the dissemination and support of these initiatives has grown significantly. Thus, the purpose of the work is to understand United Nations World Environment Day (WED) programs impact on the digital public debate using Twitter data mining. For that, an ad hoc methodology is designed to provide it to authorities and organizations that wish to analyze the impact of different initiatives or programs on society. All in all, the research carried out analyzes more than 400,000 tweets sent during the 2021 edition of the WED. The tweets have been processed using Big Data techniques and Social Network Analysis. The research reveals that the WED was a trending topic initiative that was discussed in positive terms, where collective sentiment was shown. The topics covered dealt with the event day and the different initiatives related to restoration of ecosystems. However, it is noted that: there is no coordinated action by the institutions, groups or individuals involved in the conversation and the initiative tends towards homophily; digital mobilization is mostly centered in the host country (Pakistan) and, above all, in the neighboring country (India) and, the conspicuous absence of the business sphere in the discussion.

‘We're now the Walking Dead’: Predatory Policing, Youth Agency and Framing in Nigeria’s #EndSARS Social Activism

ABSTRACT The #EndSARS protests swept through Nigeria in October 2020 providing opportunities for Nigerian youths to demonstrate against police violence, poor governance and daunting socio-economic challenges plaguing their country. Drawing on the protesters’ collective sentiments on Twitter, this article interrogates youth agency, ethics of force in policing and collective action of #EndSARS protest actors against predatory policing. It examines the deployment of digital affordances in protest movements and discusses how #EndSARS protesters initiated frames of collective action, agency and injustice to identify problems, ventilate their grievances, attribute blame, mobilise themselves and articulate solutions. Using the cultural theory of social movements, it argues that the protesters enacted frames through discursive strategies to express ideological views and achieve specific purposes. The article further employs Ruth Wodak’s discursive paradigms to investigate the dynamics of in-group and out-group categorisations in #EndSARS protest movement. The discursive mechanisms are also employed to study the creation of stereotypes, legitimisation of protest acts and mobilisation of aggrieved Nigerians against police brutality.

Knowledge Discovery from Large Amounts of Social Media Data

In recent years, social media analysis is arousing great interest in various scientific fields, such as sociology, political science, linguistics, and computer science. Large amounts of data gathered from social media are widely analyzed for extracting useful information concerning people’s behaviors and interactions. In particular, they can be exploited to analyze the collective sentiment of people, understand the behavior of user groups during global events, monitor public opinion close to important events, identify the main topics in a public discussion, or detect the most frequent routes followed by social media users. As an example of the countless works in the state-of-the-art on social media analysis, this paper presents three significant applications in the field of opinion and pattern mining from social media data: (i) an automatic application for discovering user mobility patterns, (ii) a novel application for estimating the political polarization of public opinion, and (iii) an application for discovering interesting social media discussion topics through a hashtag recommendation system. Such applications clearly highlight the abundance and wealth of useful information in many application contexts of human life that can be extracted from social media posts.