Public Sentiment Research Articles

“Back to the Kitchen”: Corruption Charges Against Women Heads of Government

Abstract While much of the scholarship on gender and corruption suggests that women in political office are, or are perceived to be, less corrupt than men, in just the past few years corruption accusations against Brazil's Dilma Rousseff and South Korea's Park Geun-hye have made headlines and led to their impeachment. In this article, we argue women heads of government are actually more likely to be charged with corruption due to pervasive beliefs that women, by their very presence, corrupt public office. Using cross-national data, we first demonstrate that women executives are significantly more likely to be formally accused of corruption than their male counterparts. We then present case studies of Brazilian President Rousseff and Turkish Prime Minister Çiller to demonstrate the powerful role of gendered discourse in motivating suspicion and inflaming elite and public sentiment and thereby driving corruption charges. These findings make a substantial contribution to the literature on gender, leadership and the politics of corruption.

A Study on the Evolution of Public Sentiment and Emotional Communication Mechanisms in Women's Issues on microblog: Case Study of "Quan XiXi' s Dialogue with Chizuko Ueno"

The research utilizes Sina microblog as its platform, analyzing textual data associated with trending topics featuring "Peking University Dormitory and Ueno Chat", to explore patterns of sentiment evolution and emotional expression. Findings reveal that public sentiment follows a four-phase trajectory: inception, peak, fluctuation, and decline. Negative sentiments dominate among users discussing the topic. This analysis elucidates microblog's role in disseminating women's issues, identifies key characteristics and challenges of such discussions on the platform, delineates major disagreements and focal points in user discourse, and endeavors to uncover underlying societal symptoms. The insights contribute valuable data to studies on the forms and impacts of women's issue dissemination on microblog.

The Approach of Finland and Sweden to the Soviet and Nazi Occupations of the Baltic States in 1940–1944.

The rapid change in the status of the Baltic States at the beginning of the 1940s, led to erratic and mixed reactions of the outside world, while the struggle of dependent nations for their statehood established a certain place for the Baltic States in the international community. The study analyses the attitude of two Nordic countries – Sweden and Finland – to the three countries that lost their independence during the period of the first two occupations in 1940–1944. So far, historians have not analysed this topic. Looking back on this relatively recent period of history, which was particularly difficult for the Baltic States, and at the position of the Nordic countries would allow to better assess cooperation opportunities of the countries of the Baltic Sea region arising in the twenty-first century and determine the importance of the Baltic States in cross-border relations in the Baltic Sea region. The aim of the study is to analyse in detail the official position of Finland and Sweden, and the attitudes of their peoples towards the Baltic States during the period of the first two occupations. The key focus of the study is analysis of the problem of recognition of the Soviet occupation and the annexation of the Baltic States by the Nordic countries. An important part of the analysis involves the study of the correspondence of Lithuanian diplomats working in the Nordic countries: this would allow a better understanding of public sentiment in Finland and Sweden regarding the two occupational regimes in the Baltic countries. The first part of the article deals with the official approach of the Finnish and Swedish governments towards Soviet occupation and annexation of the Baltic States. The analysis of the sources, political reasoning, historiography of Lithuania and foreign countries, and the press of the Nordic countries reveals that Sweden recognised the incorporation of Lithuania, Latvia and Estonia into the Soviet Union de facto by signing several agreements with Moscow. Meanwhile, Finland, due to unfavourable relations with the Soviet Union, was reluctant to give an assessment of the changed status quo of the Baltic States. The Finnish government maintained a policy of tacit non-recognition of the annexation of the Baltic States. The second part of the study explores the approach of the Nordic countries towards the Nazi occupation of the Baltic States. Finland and Sweden treated the Nazi actions in the east of the Baltic Sea region as a result of the unpredictable progress of WWII. A certain interest in the problems of the occupied Baltic States on the part of Swedish and Finnish society is revealed by publications in the Nordic press. They question the fate of the three Baltic States in a fragmented manner. The last part of the study examines the approach of the Nordic countries towards the three occupied Baltic States on the basis of correspondence of Lithuanian diplomats residing in the Nordic countries. After the Soviet occupation these Lithuanian diplomats stayed in the Nordic countries and assessed public opinions prevailing in Sweden and Finland towards the occupation. The study reveals that in Sweden, in June 1940, the actions of the Soviet Union in Lithuania, unlike those in Latvia and Estonia, were not treated as occupational. The fellow feelings expressed by representatives of the Swedish government to the Baltic States were personal in nature and did not affect the official position of Sweden. Between 1941 and 1944, Finnish society was also friendly to the three Baltic States. This was expressed in negative statements against the occupation of the Baltic States carried out by the Soviet Union. The Finnish and Swedish press paid only fragmentary attention to the issue of enforcement and change of occupational regimes in the east of the Baltic Sea region.

Design of judicial public opinion supervision and intelligent decision-making model based on Bi-LSTM

Fuzzy preference modeling in intelligent decision support systems aims to improve the efficiency and accuracy of decision-making processes by incorporating fuzzy logic and preference modeling techniques. While network public opinion (NPO) has the potential to drive judicial reform and progress, it also poses challenges to the independence of the judiciary due to the negative impact of malicious public opinion. To tackle this issue within the context of intelligent decision support systems, this study provides an insightful overview of current NPO monitoring technologies. Recognizing the complexities associated with handling large-scale NPO data and mitigating significant interference, a novel judicial domain NPO monitoring model is proposed, which centers around semantic feature analysis. This model takes into account time series characteristics, binary semantic fitting, and public sentiment intensity. Notably, it leverages a bidirectional long short-term memory (Bi-LSTM) network (S-Bi-LSTM) to construct a judicial domain semantic similarity calculation model. The semantic similarity values between sentences are obtained through the utilization of a fully connected layer. Empirical evaluations demonstrate the remarkable performance of the proposed model, achieving an accuracy rate of 85.9% and an F1 value of 87.1 on the test set, surpassing existing sentence semantic similarity models. Ultimately, the proposed model significantly enhances the monitoring capabilities of judicial authorities over NPO, thereby alleviating the burden on public relations faced by judicial institutions and fostering a more equitable execution of judicial power.

Analisis Sentimen Masyarakat terhadap Pelayanan e-KTP dengan Metode Support Vector Machine

The rapid development of information and communication technology allows society to access various information needed in daily life. The Law of the Republic of Indonesia Number 23 of 2006 concerning population administration serves as an important element in population management. Population documents are issued by official institutions and have legal legitimacy as valid evidence. The method used in this research regarding public sentiment towards e-ID card services is the survey method, which aims to collect data from a large population using a smaller sample. The steps or processes in this research using the SVM method consist of case folding, cleaning, tokenizing, normalization, stopword removal, and stemming. Based on the classification of 150 test data using SVM, the number of positive sentiments recorded is 110 opinions, while negative sentiments recorded are 40 opinions.

Research on Public Opinion Guidance Strategies of Political Microblogs in the Context of Emergencies

This study takes "Gansu 12-18 Jishishan Earthquake" as an example to explore the strategies of guiding public opinion on government microblogs in the context of emergencies. Using the life cycle theory of public opinion, the study divides the development process of public opinion into four stages: latency, growth, maturity and decline, and systematically analyzes the performance of government microblogs, such as @Gansu Firefighting, @Gansu Public Security, and @Gansu Publishing, in the development of public opinion on emergencies, as well as their guiding effects in the various stages of public opinion development. The study shows that governmental microblogs perform better in terms of timeliness and authority of information release in emergencies, and are able to rapidly convey official information and stabilize public sentiment. However, there are still deficiencies in the interactive mechanism, public opinion monitoring and early warning, and evaluation and feedback. Based on this, it is proposed to optimize the public opinion monitoring and warning mechanism, enhance public interaction, and improve the feedback and evaluation system, so as to improve the public opinion management ability of government microblogs in emergencies, and to ensure the efficient communication of government information and the effective guidance of public opinion.

Підходи до вивчення громадської думки в СРСР 1920-х –1930-х рр.

The measurement of public opinion has become an essential practice across various fields, evolving from historical methods to modern polling techniques. Politicians have long tried to understand public sentiment through tools like media analysis and direct communication. In democratic societies, this understanding helps with electoral success, while in totalitarian regimes, it often serves to maintain control through surveillance and other strategies. The article underscores the challenges scholars face in reconstructing public sentiment in oppressive environments where reliable data is scarce. Historians often depend on alternative sources, such as secret police reports, media publications, and personal accounts, to build a nuanced understanding of public opinion. Our overview has identified various approaches to studying public opinion in the USSR during the interwar period. Some studies focus on representing a range of reactions that ordinary people have to newly introduced state policies and everyday problems. Other scholars highlight complex social dynamics (active and passive resistance, adaptation, apathy, etc.) Moreover, a number of public opinion studies are devoted to the collective identity of different social groups and how it changed under the influence of Soviet propaganda and social engineering. At the same time, some researchers who heavily relied on personal accounts reconstructed people’s emotional states under the pressure of totalitarianism. Overall, exploring public sentiment in oppressive environments allows for a deeper understanding of the nature of totalitarianism and the social resistance under such regimes. Keywords: public opinion, popular opinion, public sentiments, behavior, resistance, USSR, Holodomor.

SENTIMENT ANALYSIS OF PUBLIC OPINION ON PRESIDENTIAL ADVISORY APPOINTMENTS USING NAIVE BAYES CLASSIFICATION

Social media platforms such as Twitter, Facebook, and YouTube have become significant channels for public discourse, where users freely express opinions, including negative sentiments and hate speech. To better understand public opinion, particularly in politically charged contexts, sentiment analysis can classify user comments as either positive or negative. This study aims to analyze public sentiment regarding the formation of a special advisory team for President Jokowi, using a sentiment classification approach. The study employed a Naïve Bayes classifier to analyze sentiment from 3,000 comments gathered from Twitter, Facebook, and YouTube. The dataset was divided into 80% training data (used to train the model with known sentiment) and 20% test data. The Naïve Bayes algorithm was chosen for its simplicity and effectiveness in handling large datasets in text classification tasks. The Naive Bayes classification on sentiment analysis of public opinion regarding the appointment of presidential advisors achieved an overall accuracy of 71% in classifying the test data. Negative sentiment was classified with an accuracy of 71%, while positive sentiment was classified with an accuracy of 70%. The results demonstrate that the Naïve Bayes classifier is a viable method for sentiment analysis in political discourse, although the model's performance indicates room for improvement. The novelty of this research lies in its focus on sentiment analysis of public opinion specifically related to presidential advisory appointments, an area not yet extensively explored in sentiment analysis studies. This study contributes to the field by providing insights into the public’s perception of political decisions using machine learning techniques. The implications for future research include refining classification methods for better accuracy and applying the model to other political or governmental topics.

The Role Of The Press In Shaping Public Opinion During The Freedom Struggle

The role of the press during the freedom struggle was instrumental in shaping public opinion and galvanizing the masses against colonial oppression. As India sought independence from British rule in the late 19th and early 20th centuries, newspapers, journals, and pamphlets emerged as vital tools for disseminating nationalist ideas and fostering political awareness. The press not only reported on events but also provided a platform for political discourse, allowing voices from diverse communities to contribute to the independence narrative. Great figures such as Bal Gangadhar Tilak, Mahatma Gandhi, and other leaders utilized the press to articulate their visions for a free India, framing the struggle in terms of justice and self-determination. Newspapers like "The Hindu," "The Times of India," and "The Tribune" played crucial roles in informing the public about the injustices of colonial rule, inspiring a collective consciousness among various strata of society. Editorials, articles, and letters to the editor encouraged civic engagement and mobilized support for protests, strikes, and other forms of resistance. The press faced significant challenges, including censorship and repression from colonial authorities, which often sought to suppress dissenting voices. Despite these obstacles, many journalists and editors risked their safety to publish revolutionary ideas and expose the realities of British exploitation. This resilience illustrated the press's commitment to truth and justice, fostering an environment where public sentiment could coalesce around the freedom movement. The emergence of vernacular publications further broadened the reach of nationalist sentiments, ensuring that the message of independence resonated across linguistic and cultural divides. These publications played a crucial role in educating the masses and instilling a sense of pride and identity that transcended regional differences. The press was not merely a bystander but a catalyst in the freedom struggle, shaping public opinion and uniting diverse groups against colonial rule. Its influence extended beyond immediate political outcomes, contributing to the formation of a national consciousness that would endure long after independence was achieved. This study highlights the indispensable role of the press in the socio-political landscape of the freedom struggle, illustrating its enduring legacy in contemporary society.

Sociopolitical Impacts of the Russia-Ukraine Conflict on Poland, Germany, and Turkey: A Comparative Analysis of National Responses and Public Sentiment

Abstract: The Russia-Ukraine conflict began in 2022 and represents the biggest military action in 21st century Europe. However, this conflict did not arise out of the blue. The conflict is the culmination of many centuries worth of social and political factors. Like in any war, the effects of it have not only been felt in Ukraine and Russia but also in its neighboring and more distant countries across Europe and beyond. This essay will focus on the sociopolitical impacts of the conflict on three key countries: Poland, Germany and Turkey, exploring the specific ways in which these nations have been affected both socially and politically by the ongoing crisis. This paper explores the impacts of the conflict on wider European countries, analysing how the war has affected the sociopolitical structures of Poland, Germany and Turkey. Poland has demonstrated a closer alliance towards the West to protect itself from Russian aggression, resulting in strong humanitarian support for Ukraine and public solidarity towards Ukrainian refugees. Germany has shifted its long-standing foreign policy to adopt a similar anti-Russian stance, creating public concerns with regard to being drawn into the war. Turkey has a unique perspective, appeasing both the West and Russia as it relies on both to maintain its domestic socioeconomic situation. This analysis provides insight into the wider European sociopolitical response to the conflict.

AI-Driven Optimization of Financial Quantitative Trading Algorithms and Enhancement of Market Forecasting Capabilities

Abstract: The application of artificial intelligence (AI) into financial markets has revolutionised quantitative trading and market forecasting by increasing the efficiency of algorithmic trading, improving the accuracy of market predictions and facilitating real-time market decisions. This paper will provide an overview of the application of Al in the financial markets focusing on the use of machine learning (ML), deep learning (DL) and reinforcement learning (RL) in optimizing the trading algorithms, specifically the capability of Al to process very high data points and complex relationships that other quantitative models are unable to capture. We will discuss trading algorithms such as XGBoost, deep neural networks such as long short-term memory (LSTM) networks and convolutional neural networks (CNNs), how they can outperform traditional quantitative trading models and real-time decision making in stock price prediction, pattern recognition and trading strategy optimisation. We will also look at Al-enhanced predictive models that utilise deep learning and layered models, such as Natural language processing (NLP) sentiment analysis to capture the public sentiment in the market to forecast employing diverse datasets such as historical prices, market volatility, macroeconomic factors and social media sentiment to improve the forecasting accuracy. By going through several experiments and case studies, this paper will shed light on the impact of entrusting quantitative trading and market forecasting decisions to AI for improved performance and reduced errors. There are many challenges ahead but AI plays a constructive role in improving the trading strategies and forecasting market outcomes accurately.

Variations in COVID-19 vaccine hesitancy over time: a serial cross-sectional study in five West African countries

ObjectivesThis study aims to identify the factors influencing vaccine hesitancy, willingness and its variation over time in order to inform more responsive strategies for increasing vaccination uptake. The specific objectives...

E-learning adoption: a comparative analysis of public sentiments during COVID-19

ABSTRACT The COVID-19 pandemic accelerated e-learning adoption, presenting both opportunities and challenges for educational development, particularly in the context of Information Technology for Development (ITD). This study profiles public sentiments on e-learning to identify key factors influencing IT-enabled education. A text analysis of Tweets from the United States and India posted from March 2020 to January 2022 was conducted using a computer-aided text analysis and natural language processing alongside a content analysis using a machine learning technique. Findings show positive sentiments towards e-learning from both countries, with trust, anticipation, and joy as predominant emotion categories. Themes of strongest importance are students, schools, and education as central to e-learning discussions. The study also uncovers challenges such as the need for internet infrastructure and teacher training in digital pedagogy. These findings contribute to the broader understanding of how information technologies can address educational challenges and promote development in diverse global contexts.

Exploring Sentiment Analysis for the Indonesian Presidential Election Through Online Reviews Using Multi-Label Classification with a Deep Learning Algorithm

Presidential elections are an important political event that often trigger intense debate. With more than 139 million users, YouTube serves as a significant platform for understanding public opinion through sentiment analysis. This study aimed to implement deep learning techniques for a multi-label sentiment analysis of comments on YouTube videos related to the 2024 Indonesian presidential election. Offering a fresh perspective compared to previous research that primarily employed traditional classification methods, this study classifies comments into eight emotional labels: anger, anticipation, disgust, joy, fear, sadness, surprise, and trust. By focusing on the emotional spectrum, this study provides a more nuanced understanding of public sentiment towards presidential candidates. The CRISP-DM method is applied, encompassing stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment, ensuring a systematic and comprehensive approach. This study employs a dataset comprising 32,000 comments, obtained via YouTube Data API, from the KPU and Najwa Shihab channels. The analysis is specifically centered on comments related to presidential candidate debates. Three deep learning models—Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and a hybrid model combining CNN and Bi-LSTM—are assessed using confusion matrix, Area Under the Curve (AUC), and Hamming loss metrics. The evaluation results demonstrate that the Bi-LSTM model achieved the highest accuracy with an AUC value of 0.91 and a Hamming loss of 0.08, indicating an excellent ability to classify sentiment with high precision and a low error rate. This innovative approach to multi-label sentiment analysis in the context of the 2024 Indonesian presidential election expands the insights into public sentiment towards candidates, offering valuable implications for political campaign strategies. Additionally, this research contributes to the fields of natural language processing and data mining by addressing the challenges associated with multi-label sentiment analysis.

Mapping Egyptian Translations of Israeli Literatures: Evolution, Attitudes, and Themes

This study delves into the intricate realm of Egyptian translations of Hebrew and Israeli literatures, illuminating the various attitudes of the Egyptian government, intellectual elite, and educated populace toward cultural normalization with Israel. Analyzing a corpus of over a hundred translations spanning different genres, this research outlines turning points in the evolution of translation practices from the establishment of Israel to the present day. The study unveils a complex interplay between politics, culture, and public sentiment, illustrating how translations serve various roles, ranging from instrumental polemic tools in the Israeli–Arab conflict to avenues for cultural openness and familiarity. While the prevailing norm involves refraining from publicly endorsing normalization with Israel, government policy reflects a nuanced approach that balances anti-normalization sentiments at the official level with private sector flexibility. By exploring the selection criteria governing translation, the research provides valuable insights into the preferences of the Egyptian reading public and its perceptions of Israel, peace, and intercultural exchange.

The price of attention: an analysis of the intersection of media coverage and public sentiments about eggs and egg prices.

The price of attention: an analysis of the intersection of media coverage and public sentiments about eggs and egg prices.

Revisiting time-varying dynamics in stock market forecasting: A Multi-source sentiment analysis approach with large language model

This paper presents the Heterogeneous Dynamic Seemingly Unrelated Regression with Dynamic Linear Models (HD-SURDLM), an innovative framework for stock return prediction that combines cutting-edge sentiment analysis with dynamic financial modeling. The model integrates sentiment data from 2.5 million Twitter posts and various news sources, utilizing state-of-the-art sentiment analysis tools such as VADER, TextBlob, and RoBERTa. HD-SURDLM refines Gibbs sampling for enhanced numerical stability and efficiency while capturing cross-sectional dependencies across multiple assets such as a portfolio. The model consistently outperforms traditional methods like LSTM, Random Forest, and RNN in forecasting accuracy. Empirical results show a 1.02% improvement in 1-day horizon forecasts, a 0.42% gain for 20-day predictions, and a 0.36% increase for 50-day forecasts. By effectively merging public sentiment with dynamic asset modeling, HD-SURDLM offers substantial improvements in short- and long-term prediction accuracy. Its capacity to capture both cross-sectional insights and temporal dynamics makes it an invaluable tool for investors, traders, and financial institutions navigating sentiment-driven markets. HD-SURDLM not only enhances predictive accuracy but also provides a robust decision-support system for financial stakeholders.

Public Sentiment Analysis About Neuralink from Twitter Using Naïve Bayes: Multinomial, Gaussian and Complement

Elon Musk owns the business Neuralink, which attempts to build brain-machine interfaces. This study categorizes public opinion towards the use of Neuralink goods, including whether people agree (positive), disagree (negative), or feel neither way. Without accessing the Twitter API, the Twint Python Libraries were utilised to retrieve a dataset of 3000 using the keyword “neuralink”. What datasets are included in positive, neutral, or negative categories are designated using RoBERTa. Term Frequency Inverse Document Frequency (TF-IDF) is utilized for feature extraction, while Synthetic Minority Over-sampling Technique (SMOTE) is employed to handle class imbalance. Complement Naive Bayes, achieved accuracy of 81%, followed by Multinomial Naive Bayes, which achieved accuracy of 80%, and Gaussian Naive Bayes, which achieved accuracy of 75%. The model Complement Naïve Bayes was used in this study to attain the maximum accuracy, and accuracy increases when employing SMOTE compared to other Naïve bayes variants.

Leveraging Social Media and Deep Learning for Sentiment Analysis for Smart Governance: A Case Study of Public Reactions to Educational Reforms in Saudi Arabia

The Saudi government’s educational reforms aim to align the system with market needs and promote economic opportunities. However, a lack of credible data makes assessing public sentiment towards these reforms challenging. This research develops a sentiment analysis application to analyze public emotional reactions to educational reforms in Saudi Arabia using AraBERT, an Arabic language model. We constructed a unique Arabic dataset of 216,858 tweets related to the reforms, with 2000 manually labeled for public sentiment. To establish a robust evaluation framework, we employed random forests, support vector machines, and logistic regression as baseline models alongside AraBERT. We also compared the fine-tuned AraBERT Sentiment Classification model with CAMeLBERT, MARBERT, and LLM (GPT) models. The fine-tuned AraBERT model had an F1 score of 0.89, which was above the baseline models by 5% and demonstrated a 4% improvement compared to other pre-trained transformer models applied to this task. This highlights the advantage of transformer models specifically trained for the target language and domain (Arabic). Arabic-specific sentiment analysis models outperform multilingual models for this task. Overall, this study demonstrates the effectiveness of AraBERT in analyzing Arabic sentiment on social media. This approach has the potential to inform educational reform evaluation in Saudi Arabia and potentially other Arabic-speaking regions.

Public views on open data health research in Switzerland: Implications for research and policy

Abstract Background The increasing volume of publicly available data offers health researchers opportunities to explore public sentiment through open data research on issues such as vaccination hesitancy and fake news propagation. To ensure ethical conduct and maintain public trust, assessing public perceptions of open data research in healthcare is crucial. Methods We conducted 10 focus groups across the German, French, and Italian-speaking regions of Switzerland. We presented participants with real-world examples of open data research in healthcare and guided discussions using a semi-structured format. The topic areas were: (1) using Twitter data to assess public opinion on gene-editing technology, (2) assessment of health policy documents, (3) using Twitter data to predict individual health status and (4) using YouTube data to assess public sentiment on health policies. We assessed our findings using a thematic analysis approach. Results Gathering insights from 75 citizens, four major themes emerged: (1) Expectations towards actors and open data research, such as using research findings for societal benefit, and ensuring transparency in data use through informed consent; (2) Supportive measures including communication strategies to raise awareness and inform the public about the use of publicly available data for health research; (3) Concerns about data reuse for purposes beyond the study’s objectives, especially for financial gain, and about data validity and privacy, particularly for sensitive health data; and (4) Mitigative measures to minimize harm, such as through the involvement of external oversight committees. Conclusions Our findings indicate public support for open data research in healthcare, provided that specific expectations are met. Developing a guiding framework for legitimate open data research in healthcare is a valuable next step, with a focus on enhancing public awareness through communication campaigns and providing oversight measures to foster public trust. Key messages • Open data research in healthcare relies on public data use without explicit consent, which can disconnect the research community from society. • We found that public support for open data research in healthcare is generally present, however it is also conditional on communication and policy efforts addressing their concerns.