Changes In Sentiment Research Articles

AI approach on identifying change in public sentiment for major events: Dubai Expo 2020

Existing research in identifying changes in public sentiment for major events faces limitations in capturing the intricacies of temporal changes, especially with large-scale data and diverse linguistic nuances. This study addresses this problem, with a particular focus on the globally acclaimed Dubai Expo 2020. The methodology employed in this research introduces a groundbreaking approach by leveraging advanced data engineering techniques on a massive corpus of 118,025 Tweets obtained from diverse users across 60 languages, covering the period from April 2021 to March 2023. The study pioneers the application of artificial intelligence (AI), natural language processing (NLP), and Large Language Models (LLMs) for sentiment analysis and topic modeling on Twitter discourse. Through the meticulous data engineering process, including language detection, dynamic translation, and sentiment analysis, the research identifies subtle yet statistically significant changes in public sentiment, as evidenced by ANOVA testing (p=0.018 in average positive sentiment, p=0.004 in average neutral sentiment, and p=0.005 in average negative sentiments). Additionally, the study innovatively extracts and analyzes support-related Tweets, revealing distinct phases in temporal domains (pre-Expo, Expo, Post-Expo1, and Post-Expo2) and yielding 11,116 support-related tweets. The application of NLP techniques further uncovers 19 topics from UAE-related Tweets, providing a comprehensive understanding of the dynamic landscape of public sentiment over a two-year period. This research contributes significantly to the field by offering a novel and comprehensive framework for analyzing public sentiment, particularly in the context of major events, and sheds light on its broader implications for event management and public perception analysis.

SNSMiner_VAC: Analyzing vaccination based on social network service data for safety surveillance

COVID-19 has created a major issue around the world, posing a serious threat to socioeconomic and human health, and there is still fear of new infectious diseases like this. In Korea, in response to COVID-19, the COVID-19 vaccine approved by the Ministry of Food and Drug Safety was sequentially administered to each group, raising national interest in the safety, side effects, and effectiveness of the vaccine, and securing the basis for related policies. Many attempts have been made for this. We are interested in a methodology to establish a scientific basis for how to respond to issues regarding vaccine stability due to the emergence of new infectious diseases. In this study, we use comments posted on Naver, the largest social platform in Korea, to obtain consumer sentiment information about vaccines and understand the change in Korean public sentiment in the first half of 2021 for each vaccine based on the analysis pipeline (SNSMiner_VAC). We investigated the Korean public’s response to the COVID-19 vaccination on social media from November 30, 2021, to January 31, 2022. We collected comments related to the COVID-19 vaccination using the Korean words for “COVID-19 vaccination” as keywords. Of the 329,559 comments, 240,322 were analyzed after preprocessing. We developed three lexicons (stopwords, sentiment words, and custom words related to COVID-19) for Korean natural language processing. Furthermore, we extracted five clusters — including symptoms, side effects, vaccine hesitancy, pro-vaccination, and infectious disease control policy — through network analysis. The sentiment analysis revealed that negative public opinion was prominent even after the commencement of the phased recovery of daily life. From these results, we were able to automatically collect data-based information on patients’ symptoms and side effects for the new vaccine and prepare and analyze the basis for collecting public opinions about vaccination. We hope that the pipeline proposed in this study can be used as a tool to create a data source for effective management policies for vaccines being developed against the emergence of new infectious diseases.

Business Confidence in Indonesia: Which Macroeconomic Factors Have Long-Term Impact?

Business confidence refers to the level of optimism or pessimism that business owners have about the prospects of their companies and the overall economy. Thus, the focus of this study is to examine the long-term impact of various macroeconomic factors—economic growth, government expenditure, interest rates, inflation, exchange rates, and the composite stock price index—on the business confidence index in Indonesia by utilizing monthly data from January 2009 to December 2022. We employ Dynamic Ordinary Least Squares (DOLS) and Fully-Modified Ordinary Least Squares (FMOLS) as the main methods, with Canonical Cointegrating Regressions (CCR) as a robustness check method. The study also utilizes pairwise Granger causality tests for a comprehensive analysis. The findings indicate that all macroeconomic factors significantly impact the business confidence index in the long term across all methodologies. Specifically, economic growth, inflation, and the composite stock price index exert a positive impact, while government expenditure, interest rates, and exchange rates indicate a negative impact on the business confidence index. This evidence emphasizes the importance for businesses to diligently monitor macroeconomic trends and understand the patterns in these indicators so that companies can better anticipate changes in business sentiment. Taking a long-term perspective when making strategic decisions and investments is also advisable, recognizing that the influence of macroeconomic factors on business confidence may be more pronounced over time.

Dynamics of Indonesian Public Opinion on the Rohingya Crisis in Time Perspective Using Traditional Machine Learning and Deep Learning

The Rohingya are an ethnic minority who currently still face persecution and discrimination in Myanmar, so they have to flee to neighboring countries, such as Indonesia. However, the polemic regarding the issue of the existence of Rohingya refugees in Indonesia still shows that there are differences of opinion between groups who support and oppose it. For this reason, this research aims to determine the dynamics of Indonesian public opinion regarding the Rohingya from 2015-2023 via Twitter, as well as find out the topics that are often discussed each year using LDA. This research compares classification methods using traditional machine learning algorithms (NB, SVM, LR, and DT) and deep learning algorithms (LSTM, GRU, LSTM-GRU, and GRU-LSTM). The research results show that the traditional machine learning algorithm, LR, has the highest accuracy. There has been a change in sentiment from initially being dominated by positive sentiment to negative sentiment which is more dominant in the last five years. The topics that are often discussed for positive sentiment are the support of the Indonesian people for the Rohingya in providing assistance and shelter, while the negative topics are related to concerns about the social, economic, and security impacts that may be caused by the presence of Rohingya refugees.

Sentiment and thematic analysis of faculty responses: Transition to online learning

This project aims to understand physics faculty responses to transitioning to online teaching during the COVID-19 pandemic. We surveyed 662 physics faculty from the United States following the Spring 2020 term; of these, 258 completed a follow-up survey after the Fall 2020 term. We used natural language processing to measure the sentiment scores of 364 Spring 2020 responses and another 134 Fall 2020 responses of physics faculty who completed an optional written prompt. Additionally, we determined the change in sentiment scores of the 100 individuals who responded to both surveys. These sentiment scores measured between −1 and 1 for completely negative and completely positive, respectively. Sentiment scores after Spring 2020 were slightly positive with a median value of 0.2347. The distribution of sentiment changes was approximately normally distributed with a mean centered near zero. Analysis suggests the average sentiment did not change from the initial to follow-up surveys. To identify major topics within the responses for both surveys, latent Dirichlet allocation analysis was applied to the data. The topic distribution for the initial survey is given as course modifications and technology, negative aspects of the transition—primarily with labs and cheating, exam and evaluation difficulties, and difficulties with student understanding. The topics were noticeably different in the follow-up survey with differences between Fall and Spring, cooperative learning strategies, strategies that worked in the remote space, and benefits of in-person labs. Published by the American Physical Society 2024

The Spillover Effect of Stock Market Sentiment Intensity and Uncertainty on Housing Market in the United States

Investor sentiment is a key indicator of market performance. Many have studied how investor sentiment affects market returns or returns of different categories of stocks. Besides, many also studied how information revealed in media can affect the housing market. This paper focuses on the sentiment revealed in social media, Tweet, by combining these two key factors that have not been studied. This paper studies the spillover effect of stock market sentiment on the housing market by applying Vector Autoregression. The VAR model helps explain the relationship between housing prices and stock market sentiment from tweets, where sentiments are divided into sentiment intensity and sentiment uncertainty. The series of sentiment intensity and series of housing prices interact with each other, and the change in sentiment in the last period spills over to the housing market which affects housing prices, but the change in housing price does not affect sentiment in reverse. Similarly, the series of sentiment uncertainty and the series of housing prices interact with each other, and the change in sentiment diversity in the last period also spills over to the housing market which affects housing prices, but the change in housing price does not affect sentiment in reverse.

Effectiveness of Emergency Response to Typhoons: Lessons Learned from Historical Typhoon Disasters in Zhuhai, China

Typhoons significantly impact socio-economics, varying in impact due to unique characteristics. However, limited studies explore why similar intensity typhoons affect regions differently. This study compares the emergency response measures of Zhuhai, Guangdong Province, China, in response to typhoon Hato (2017), Mangkhut (2018), and Higos (2020). These three similar typhoons offer an empirical opportunity to assess the effectiveness of emergency management. This research builds a timeline of Zhuhai's emergency response measures to the typhoons, collects Weibo data 72 hours before and after the typhoon landfall, performs keyword frequency analysis on contents and analyzes in detail the content of weibo postings, and changes in sentiment. Results show that timely and systematic emergency response measures may reduce disaster losses, which means emergency management is crucial in mitigating typhoon impacts, offering valuable lessons for other coastal cities.

The Impacts of Social Media on Changing Mass Attitudes Towards Asian Hate in the U.S. During the COVID-19 Outbreak in the U.S.

In this study, our primary research focus was on analyzing the changes in anti-Asian sentiment within the United States during the mid-to-late stages of the outbreak. The authors conducted a critical examination of the context surrounding the 2019 outbreak to explore its correlation with the escalation of anti-Asian sentiment in the United States. First, it is found that historical perceptions, such as the Native American concept of "yellow peril" stemming from the Opium War and the notion of "white privilege" within the mindset of white individuals, could have contributed to the emergence of the anti-Asian phenomenon. Furthermore, the discourse that unfolded on the Internet in response to the COVID-19 pandemic resulted in changes in native Americans perceptions of Asian hate. Our research identified a noticeable surge in racially motivated hate crimes during the pandemic, as indicated by publicly available data. These incidents were predominantly characterized by instances of physical violence and harassment. Asian hatred has seriously disrupted the daily lives of Asian residents in the U.S. This phenomenon may have arisen from the general belief among most Americans that the pandemic originated in Asia, coupled with long-standing tendencies to view Asians as outsiders irrespective of their duration of residence in the U.S. To address these issues, our study aims to compare trends in anti-Asian sentiment before and after the epidemic by analyzing Twitter data from both periods. The authors also conducted a comparative analysis of relevant literature and books to formulate hypotheses explaining the variations in Americans' attitudes toward anti-Asian sentiments across the two timeframes. Ultimately, our goal is to advocate for the cessation of such unequal behavior and the establishment of equality for all.

Sentiment Analysis of COVID-19 Tweets using Neural Network in Pakistan

Covid-19 is a respiratory infectious disease that spreads from person to person. World Health Organization declared it a pandemic in March 2020. Social distancing and lockdowns during the pandemic affected the lives, sentiments, and mental health of people. They are mentally, physically, and emotionally disturbed. It was observed that the verification of the very first Covid case increased the precariousness and fear in Pakistan because people had no information about the state policies. In this paper, Covid-related tweets from Pakistan for the year 2021 are collected. Pakistan faced two Covid waves in 2021. Dataset is preprocessed and a neural network-based model is applied to analyze the sentiment of the tweet. Sentiments of people are analyzed separately for each Covid wave, moreover, a comparative analysis is presented to discuss the change in people's sentiments with the passage of time. Results show a reduction in the number of tweets from third to fourth wave. Moreover, the %decrease in negative and positive sentiments are 6 and 1 respectively which show that by the passage of time, people started living with the environment and time healed the wounds of fears and uncertainty in their lives.

Digital Mass Hysteria during Pandemic? A Study of Twitter Communication Patterns in the US during the Stages of COVID-19 Vaccination.

This study examined the public's sentiments about vaccines by analyzing Twitter data during the CDC's vaccination management planning stage in the United States. Sentiment scores were assigned to each tweet using a sentiment dictionary and the sentiment changes were analyzed over 52 weeks from November 2020 to November 2021. An interrupted time series model was used to analyze the difference in sentiment, which revealed that there was a shift. Initially, overall sentiments were negative but became positive as the stage of general vaccine supply approached. However, negative sentiments sharply rose when the vaccine supply transitioned to the phase of universalization. The results identified two dominant strategic action fields for vaccines providing polarized messages on Twitter and the negative trend was strong for most of the period. The findings highlight the importance of managing strategic action fields on social networks to prevent mass hysteria during vaccine policy implementation. This study stresses the significance of effectively managing strategic action fields on social media platforms to prevent mass hysteria while implementing vaccine policies.

Impact of integrated reporting on firm value and earnings quality as a moderator in Southeast Asia

The study analyzes the factors influencing integrated reporting and its implications for firm value with earnings quality as a moderating variable. The study was conducted on energy sector companies on stock exchanges of several Southeast Asian countries. The selection is due to Southeast Asia’s vulnerability to global market sentiment changes related to financial and sustainability aspects. The study employed the SEM-PLS analysis method. 208 data from 26 companies over 8 years were used. The investigation affirms that leverage, age, and board size have positively impacted integrated reporting. Firm size, growth, and board independence have a negative impact on integrated reporting. Profitability, board activity, and stakeholder pressure have not significantly influenced integrated reporting, but integrated reporting positively impacts firm value. Additionally, earnings quality does not moderate the influence of integrated reporting on firm value. The study provides insights for companies to improve the presentation of high-quality information to stakeholders. Increasing the firm value of energy companies in Southeast Asian countries needs to be done as a progressive concern for environmental impacts and sustainably creating integrated reporting. 

Evolution of COVID-19 Impact on Russian Stock Market: Panic Effect

Over the past few years, many research papers have referred to stock market volatility in relation to investor attention and sentiment and our article adds to the current literature on financial market reactions to the economic consequences of COVID-19. An event such as an outbreak of an infectious disease causes a negative change in investor sentiment, which strongly influences their investment decisions and, consequently, stock market prices. The subject of the study is the mutual influence of stock market characteristics and market sentiment, during a COVID-19 pandemic crisis. The purpose of the study is to provide empirical support for the hypothesis of indirect impact of uncertainty and panic under the COVID-19 pandemic on the dynamics of the stock market in Russia. The World Health Organization and experts forecast that the world will face more than one crisis related to the spread of infectious diseases in the future, so understanding the mechanisms of mutual influence of sentiment and financial markets remains relevant. In this study, we take a novel approach to deriving an indicator for panic that has not been used before. We perform econometric modeling using a Vector Autoregressive Model (VAR) and a Vector Error Correction Model (VECM), which allows us to describe in the model not only the long-term equilibrium but also the dynamics towards it. As a result, we got consistent and efficient estimates of the long-term and short-term effects of panic and mortality rates on the volatility of the RTS stock index and found that the market reaction to COVID-19 changed as the pandemic spread: the effects of uncertainty and panic, while having a significant impact at the beginning of the crisis, faded away. The conclusions obtained in the analysis of the Russian stock market dynamics coincide with those obtained by other authors in their analysis of markets in other countries over a similar period.

Hotel Rating Prediction System Based on Time Factors

While the internet provides abundant information, it often leads to information overload of users when purchasing goods. Tripadvisor.com, despite having a date sorting function, struggles to effectively filter relevant comments to users and neglects that consumer preferences may change over time. Therefore, this study aims to develop a website with visual charts showing changes in sentiment over time in reviews. The goal is to determine if this website improves user efficiency compared to the original website, reducing search time and aiding decision-making. The chart generation process involves four stages: collecting and preprocessing comments, constructing a hotel feature dictionary, classifying sentences and computing sentiment scores, and embedding charts on the website. 36 Tripadvisor.com users participate in experiments to evaluate the impact of old and new interfaces on answer quantity and search time. The NASA.tlx scale is used to assess the mental load experienced with both interfaces.

The asymmetric response of sovereign credit default swaps spreads to risk aversion, investor sentiment and monetary policy shocks

This paper investigates the determinants of sovereign credit default swaps spreads in the case of European countries. More specifically, it purposes to yield insight into how sovereign credit default swaps spreads respond to changes in investor sentiment, risk aversion, and monetary policy shocks during different market states. Using monthly data from May 2009 to December 2021 and running the quantile regression model, we show that risk aversion, investor sentiment and monetary policy affect the sovereign credit risk changes, and their effects vary across countries and markets states. More importantly, Risk aversion and monetary policy display the major asymmetric role in explaining CDS premia. However, limited asymmetric explanatory power is shown for investor sentiment. Furthermore, our findings demonstrate that the recent European debt crisis has significantly shaped the sensitivity of the sovereign credit risk spreads to the studied determinants. The findings of this study are helpful for investors in forecasting sovereign European credit spreads and hedging the countries default risk.

Role of Synchronous, Moderated, and Anonymous Peer Support Chats on Reducing Momentary Loneliness in Older Adults: Retrospective Observational Study.

Older adults have a high rate of loneliness, which contributes to increased psychosocial risk, medical morbidity, and mortality. Digital emotional support interventions provide a convenient and rapid avenue for additional support. Digital peer support interventions for emotional struggles contrast the usual provider-based clinical care models because they offer more accessible, direct support for empowerment, highlighting the users' autonomy, competence, and relatedness. This study aims to examine a novel anonymous and synchronous peer-to-peer digital chat service facilitated by trained human moderators. The experience of a cohort of 699 adults aged ≥65 years was analyzed to determine (1) if participation, alone, led to measurable aggregate change in momentary loneliness and optimism and (2) the impact of peers on momentary loneliness and optimism. Participants were each prompted with a single question: "What's your struggle?" Using a proprietary artificial intelligence model, the free-text response automatched the respondent based on their self-expressed emotional struggle to peers and a chat moderator. Exchanged messages were analyzed to quantitatively measure the change in momentary loneliness and optimism using a third-party, public, natural language processing model (GPT-4 [OpenAI]). The sentiment change analysis was initially performed at the individual level and then averaged across all users with similar emotion types to produce a statistically significant (P<.05) collective trend per emotion. To evaluate the peer impact on momentary loneliness and optimism, we performed propensity matching to align the moderator+single user and moderator+small group chat cohorts and then compare the emotion trends between the matched cohorts. Loneliness and optimism trends significantly improved after 8 (P=.02) to 9 minutes (P=.03) into the chat. We observed a significant improvement in the momentary loneliness and optimism trends between the moderator+small group compared to the moderator+single user chat cohort after 19 (P=.049) and 21 minutes (P=.04) for optimism and loneliness, respectively. Chat-based peer support may be a viable intervention to help address momentary loneliness in older adults and present an alternative to traditional care. The promising results support the need for further study to expand the evidence for such cost-effective options.

Quantile connectedness between China’s new energy market and other key financial markets

ABSTRACT This study utilizes data from the China Securities Index for the period from September 2017 to September 2022 and employs the quantile VAR model’s connectedness measurement method to examine the static and dynamic quantile connectedness between China’s new energy sector and other financial markets. The research findings reveal a significant correlation between China’s new energy market and other financial markets. During stable market periods, China’s new energy market exhibits strong predictive power over changes in other financial markets and acts as a net transmitter of systemic return shocks. The new energy, aviation, and agriculture sectors have a substantial impact on other financial markets. Extreme shocks significantly influence spillover effects, with total connectedness levels increasing with extreme downward and upward shocks. Furthermore, in the rolling window analysis, the total risk spillover index exhibits pronounced fluctuations during extreme market conditions, indicating that changes in investor sentiment and market expectations lead to greater market volatility and more significant information transmission effects. The new energy industry demonstrates predictable risk transmission characteristics, typically acting as a net transmitter of systemic risk in both normal and extreme market conditions.

LEET: stock market forecast with long-term emotional change enhanced temporal model.

The stock market serves as a macroeconomic indicator, and stock price forecasting aids investors in analysing market trends and industry dynamics. Several deep learning network models have been proposed and extensively applied for stock price prediction and trading scenarios in recent times. Although numerous studies have indicated a significant correlation between market sentiment and stock prices, the majority of stock price predictions rely solely on historical indicator data, with minimal effort to incorporate sentiment analysis into stock price forecasting. Additionally, many deep learning models struggle with handling the long-distance dependencies of large datasets. This can cause them to overlook unexpected stock price fluctuations that may arise from long-term market sentiment, making it challenging to effectively utilise long-term market sentiment information. To address the aforementioned issues, this investigation suggests implementing a new technique called Long-term Sentiment Change Enhanced Temporal Analysis (LEET) which effectively incorporates long-term market sentiment and enhances the precision of stock price forecasts. The LEET method proposes two market sentiment index estimation methods: Exponential Weighted Sentiment Analysis (EWSA) and Weighted Average Sentiment Analysis (WASA). These methods are utilized to extract the market sentiment index. Additionally, the study proposes a Transformer architecture based on ProbAttention with rotational position encoding for enhanced positional information capture of long-term emotions. The LEET methodology underwent validation using the Standard & Poor's 500 (SP500) and FTSE 100 indices. These indices accurately reflect the state of the US and UK equity markets, respectively. The experimental results obtained from a genuine dataset demonstrate that this method is superior to the majority of deep learning network architectures when it comes to predicting stock prices.

‘구례5일장’ 사례로 본 전통시장 인식 및 감성의 변화: 블로그 텍스트 마이닝 적용

‘구례5일장’ 사례로 본 전통시장 인식 및 감성의 변화: 블로그 텍스트 마이닝 적용

Stock price prediction for Google based on LSTM model with sentiment analysis

Data analytics is increasingly widely used in economic and financial fields, with one of the more important applications being the prediction of stock price changes. However, the prediction of stock price changes is challenging because stock price changes are often uncertain and affected by multiple factors. This study is designed to use the LSTM model to predict stock price changes, and in the construction of the model to consider the psychological and emotional changes of investors, adding a sentiment analysis, combined with the sentiment index obtained from the sentiment analysis and the original stock price data as the input data for the prediction model. During the experiment, a comparison experiment was set up, i.e., only using the basic LSTM prediction model to predict stock price changes and the improved LSTM prediction model with the sentiment index obtained from the added sentiment analysis to predict stock price changes. After the comparison, the prediction results obtained by the LSTM model with the addition of sentiment analysis are more accurate, which on the one hand indicates that the change of investors' psychological sentiment will have an impact on the stock price change, and indicates that the prediction results obtained by the prediction model that considers the change of investors' sentiment are more accurate. The improved LSTM prediction model can help investors to effectively avoid possible risks when investing in stocks and thus gain more profit.

Quickly Assess the Acceptability Sentiment of White Paracetamol Intake Using KNN-SMOTE Based On Receptive Deciding

Quickly Assess the Acceptability Sentiment of White Paracetamol Intake Using KNN-SMOTE Based On Receptive Deciding