Negative Sentiment Research Articles

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.

Mapping global public perspectives on mRNA vaccines and therapeutics.

The development and rollout of mRNA vaccines during COVID-19 marked a significant advancement in vaccinology, yet public hesitation to vaccination was prevalent, indicating the potential risk that future mRNA-based medical innovations will fail to be adopted. Utilizing a combined approach of large language models with manual validation and unsupervised machine learning, we conducted a social listening analysis to assess attitudes towards mRNA vaccines and therapeutics on Twitter from June 2022 to May 2023, contrasting online perspectives with data from the Vaccine Adverse Event Reporting System. Our findings reveal widespread negative sentiment and a global lack of confidence in the safety, effectiveness, and trustworthiness of mRNA vaccines and therapeutics, with frequent discussions of severe vaccine side effects, rumors, and misinformation. This underscores the need for targeted communication strategies to foster acceptance of medical treatments and strengthen public trust in order to enhance societal resilience to future health challenges.

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.

ASPECT-BASED SENTIMENT ANALYSIS ON TINDER APP REVIEWS USING THE SUPPORT VECTOR MACHINE METHOD

The research was conducted to evaluate the performance of the Support Vector Machine (SVM) in classifying sentiment in Tinder app user reviews, focusing on four aspects: price, app features, account security, and non-aspect. Of the 1627 reviews analyzed, 1138 were used for training and 489 for testing. Performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results indicated that SVM performance varied depending on the aspect analyzed. The highest accuracy was achieved in the account security aspect, with a score of 0.944, while the lowest accuracy was found in the app features aspect, at 0.8998. The highest precision for negative and neutral sentiments was observed in the account security aspect, while precision for positive sentiment was the lowest. Conversely, the highest recall was found for neutral sentiment, particularly in the app features and account security aspects. However, recall for positive sentiment in the app features aspect was very low, indicating the model's difficulty in detecting positive reviews. Overall, SVM demonstrated good performance, especially in the account security aspect and neutral sentiment, but faced challenges in classifying positive sentiment in the app features aspect.

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.

Peran Media Sosial dalam Mempengaruhi Kebijakan Pendidikan

This research aims to analyze the role of social media in influencing education policy in the digital era. This research uses a mixed approach, namely qualitative and quantitative, involving 300 respondents from various stakeholders, including students, parents, educators, and government officials. The data collection techniques that will be used in this research include surveys, interviews, and content analysis. The analysis used in this research employs SPSS for descriptive data and thematic analysis methods for interview results. The research results show that social media serves as an effective tool in disseminating information related to education policies, with 89% of respondents agreeing with this. Additionally, 70% of respondents stated that social media facilitates public participation in the education policy-making process. There is doubt about the extent to which input from social media is considered by policymakers, with 40% of respondents expressing skepticism about this. An analysis of the content of 300 posts related to education policy revealed that issues such as educational gaps, curriculum, and access to technology in online learning are top concerns for the community, with 75% of posts showing negative sentiment towards existing education policies. These findings show the importance of increasing digital literacy and creating a more inclusive mechanism so that people's voices on social media are more accommodated in policymaking

Exploring the Impact of Workforce Diversity on Employee Attitudes and Organizational Performance: An Empirical Study of Perceptions and Outcomes

Background: As modern organizations increasingly prioritize workforce diversity, understanding its influence on employee performance and organizational effectiveness is crucial. This study investigates the impact of diversity dimensions, including gender, age, ethnicity, and educational background, on employee attitudes and workplace outcomes. Objectives: The primary aim is to assess employees' perceptions of diversity within organizational contexts, exploring their attitudes toward diversity initiatives, personal impacts, and the broader effects on organizational success. Methods: A quantitative survey approach was employed, with a structured questionnaire administered to capture responses from a diverse participant sample. The survey examined agreement or disagreement across statements related to diversity's benefits, costs, and effects on organizational harmony and personal satisfaction. Findings: Results reveal a strong, positive perception of diversity among respondents, with the majority indicating that diversity fosters harmony, organizational effectiveness, and personal enrichment. While 91.4% express enthusiasm for diversity, 100% agree that diversity initiatives are worthwhile and support continued organizational efforts. Negative sentiments were minimal, indicating broad acceptance and positive attitudes toward diversity. Conclusion: The findings highlight diversity as a strategic asset that enhances innovation, inclusivity, and organizational performance. The study underscores the value of fostering a diverse workplace culture where employees feel appreciated, engaged, and motivated to contribute unique perspectives. Novelty: This research adds to the growing body of literature by providing empirical evidence from a varied demographic context, reinforcing diversity’s role in strengthening organizational cohesion and performance. The insights contribute to a deeper understanding of how positive diversity perceptions can be leveraged as a driver of organizational success.

Impact of using derivatives on stock market liquidity

This study demonstrates that using derivatives can significantly improve stock liquidity. We conduct several tests to check for robustness of our findings and control for potential endogeneity in our results. We observe that the decrease in stock illiquidity due to derivative usage is more pronounced for firms with high information asymmetry, high firm-specific risk, and negative investor sentiment. Ownership stakes held by foreign institutional investors and domestic promoters do not influence how derivative usage affects stock liquidity. This relationship is significant in the presence of large and independent boards. Our results emphasize the liquidity creation role of derivative usage, which complements other functions of derivatives markets, such as price discovery and risk management. Our findings are relevant for companies operating in foreign capital markets and for international investors who include Indian stocks into their portfolios.

Primary School Teachers’ Attitudes, Perceptions and Self-Efficacy Regarding Students With Special Educational Needs: A Comparative Study of the Education Systems in Slovenia and Kosovo

The purpose of this paper is to explore teachers’ attitudes, perceptions and self-efficacy regarding students with special educational needs in Slovenia and Kosovo, as well as to identify the similarities and differences between the two education systems in terms of inclusive practices. Quantitative data were collected from a sample of teachers working in grades 1–5 of primary schools in Slovenia (n = 258) and Kosovo (n = 255). The data were gathered using the Sentiments, Attitudes and Concerns about Inclusive Education scale and the Teacher Efficacy for Inclusive Practice scale and analysed using SPSS version 21.0. The findings indicate higher means of samples in both Slovenia and Kosovo in negative sentiments, attitudes and perceptions regarding inclusive education, while statistically significant differences were identified in attitudes and other factors, such as age, gender or teaching experience with students with special educational needs. Teachers in Slovenia and Kosovo scored higher in self-efficacy, which is negatively correlated at a significant level with sentiments, attitudes and perceptions regarding inclusive education.

Sentiment Analysis of Hotel Reviews in Anyer Beach Tourism Area: A Lexicon-Based Text Mining Approach

This study aims to analyze tourist sentiment towards hotels in the Anyer beach tourism area through online reviews. Sentiments and emotions expressed in these reviews were analyzed using a lexicon-based approach from the National Research Council (NRC) lexicon, which includes emotion categories such as anger, fear, sadness, joy, and disgust. Hotel review data were collected from TripAdvisor, and the analysis involved text preprocessing steps such as cleaning, case folding, stemming, and stopword removal. The sentiment analysis results reveal that most tourists provided positive reviews, with only 14.06% of the reviews displaying negative sentiment. Word cloud visualization identified the words "bad" and "dirty" as frequently appearing in negative reviews, likely pointing to cleanliness and service quality issues as the primary reasons for dissatisfaction. Emotional analysis showed dominant feelings of sadness, anger, disgust, and fear, likely triggered by unmet expectations regarding cleanliness and comfort during their stay. Based on these findings, it is recommended that hotel management in the Anyer beach tourism area address these concerns by implementing regular cleaning routines and staff training to enhance hotel service quality.

Portrayals of Chinese companies in American and British economic news tweets during China’s macroeconomic transitions 2007–2023

This study investigates how Chinese companies are portrayed in American and British economic news tweets, as well as their relationship to Chinese economic fluctuations. The analysis included a corpus of 55,394 tweets (934,155 words) from well-known media outlets between 2007 and 2023. It also incorporated China’s quarterly Gross Domestic Product (GDP) and monthly Purchasing Managers’ Index (PMI) to contextualise the tweets in terms of their actual economic performance. Using van Dijk’s ingroup and outgroup ideologies, this project examined the ideological depiction through sentiments and emotions. RoBERTa-based transformer models were used to analyse sentiments and emotions, whereas Large Language Models (LLMs) were used for evaluative target annotation. Positive and negative sentiments were found to be significantly (p < 0.01) correlated with China’s macroeconomic indices. The representation of Chinese companies trended between ingroup and outgroup portrayals. Positive sentiment diminished as the economy transitioned from expansion to contraction, while negative sentiment increased. American news tweets were most positive during economic balance and most negative during downturns, while British news tweets were most positive during stability or early recovery and most negative when a downturn was predicted. As the economy shifted from growth to decline, positive sentiment emphasised corporate external opportunities alongside corporate strengths, with both evaluative targets reinforcing ingroup representation. Negative sentiment shifted from corporate weaknesses to contextual threats, both strengthening outgroup representation. Moreover, certain emotions had a significant (p < 0.0001) influence on sentiment swings and ideological transitions. These findings emphasise how economic changes and journalistic factors influence the ideological representation of Chinese firms in economic news on social media.

Decoding nationalistic biases in the Grand Ethiopian Renaissance Dam research

ABSTRACT The Nile Basin, supporting 40% of Africa's population, faces a critical challenge in aligning developmental goals with water security needs. The Grand Ethiopian Renaissance Dam (GERD) illustrates this dilemma: it promises substantial energy benefits for Ethiopia but raises downstream water scarcity concerns, particularly in Egypt. This study examines GERD-related research to understand how academic perspectives may influence regional tensions or foster collaboration. Through systematic analysis of 79 articles published between 2011 and 2023, we utilized quantitative sentiment analysis to explore potential biases linked to researcher nationality, methodology, and co-authorship diversity. Findings reveal a statistically significant nationalistic bias, with Egyptian and Ethiopian researchers showing negative and positive sentiments, respectively, suggesting research can inadvertently shape polarized public and political stances on GERD. Importantly, studies with diverse co-authorship displayed reduced bias, underscoring the value of multi-national collaboration in mitigating partiality. Contrary to expectations, qualitative methods did not necessarily correlate with extreme positions, while quantitative methods appeared vulnerable to unintended bias. These insights highlight the need for critically assessing academic objectivity in transboundary resource issues, advocating for interdisciplinary and collaborative research to build mutual trust, which could potentially inform and improve regional diplomatic efforts.

Uncovering Key Factors That Drive the Impressions of Online Emerging Technology Narratives

Social media platforms play a significant role in facilitating business decision making, especially in the context of emerging technologies. Such platforms offer a rich source of data from a global audience, which can provide organisations with insights into market trends, consumer behaviour, and attitudes towards specific technologies, as well as monitoring competitor activity. In the context of social media, such insights are conceptualised as immediate and real-time behavioural responses measured by likes, comments, and shares. To monitor such metrics, social media platforms have introduced tools that allow users to analyse and track the performance of their posts and understand their audience. However, the existing tools often overlook the impact of contextual features such as sentiment, URL inclusion, and specific word use. This paper presents a data-driven framework to identify and quantify the influence of such features on the visibility and impact of technology-related tweets. The quantitative analysis from statistical modelling reveals that certain content-based features, like the number of words and pronouns used, positively correlate with the impressions of tweets, with increases of up to 2.8%. Conversely, features such as the excessive use of hashtags, verbs, and complex sentences were found to decrease impressions significantly, with a notable reduction of 8.6% associated with tweets containing numerous trailing characters. Moreover, the study shows that tweets expressing negative sentiments tend to be more impressionable, likely due to a negativity bias that elicits stronger emotional responses and drives higher engagement and virality. Additionally, the sentiment associated with specific technologies also played a crucial role; positive sentiments linked to beneficial technologies like data science or machine learning significantly boosted impressions, while similar sentiments towards negatively viewed technologies like cyber threats reduced them. The inclusion of URLs in tweets also had a mixed impact on impressions—enhancing engagement for general technology topics, but reducing it for sensitive subjects due to potential concerns over link safety. These findings underscore the importance of a strategic approach to social media content creation, emphasising the need for businesses to align their communication strategies, such as responding to shifts in user behaviours, new demands, and emerging uncertainties, with dynamic user engagement patterns.

Correlations and Fractality in Sentence-Level Sentiment Analysis Based on VADER for Literary Texts

We perform a sentence-level sentiment analysis study of different literary texts in English language. Each text is converted into a series in which the data points are the sentiment value of each sentence obtained using the sentiment analysis tool (VADER). By applying the Detrended Fluctuation Analysis (DFA) and the Higuchi Fractal Dimension (HFD) methods to these sentiment series, we find that they are monofractal with long-term correlations, which can be explained by the fact that the writing process has memory by construction, with a sentiment evolution that is self-similar. Furthermore, we discretize these series by applying a classification approach which transforms the series into a one on which each data point has only three possible values, corresponding to positive, neutral or negative sentiments. We map these three-states series to a Markov chain and investigate the transitions of sentiment from one sentence to the next, obtaining a state transition matrix for each book that provides information on the probability of transitioning between sentiments from one sentence to the next. This approach shows that there are biases towards increasing the probability of switching to neutral or positive sentences. The two approaches supplement each other, since the long-term correlation approach allows a global assessment of the sentiment of the book, while the state transition matrix approach provides local information about the sentiment evolution along the text.

Does Price Personalization Ethically Outperform Unitary Pricing? A Thought Experiment and a Simulation Study

AbstractMerchants often use personalized pricing: they charge different consumers different prices for the same product. We assess the ethicality of personalized pricing by generalizing and extending an earlier model by Coker and Izaret (Journal of Business Ethics 173:387–398, 2021) who found that price personalization ethically outperforms unitary pricing. Using a simulation analysis, we show that these results crucially depend on the choice of parameters and do not hold universally. We further incorporate additional sources of marginal cost into the utility function that will likely arise from personalized pricing. These include the expectation that personalized pricing is widely considered unfair by consumers who prefer that all consumers are charged the same price (unitary pricing), and that firms often approximate the consumers’ willingness-to-pay in ways that may raise negative sentiments among consumers who feel that their privacy is breached. By extending our model with disutility from unfairness perception and disutility from surveillance aversion, we demonstrate that personalized pricing is quickly outperformed by unitary pricing under social welfare functions that tend to prioritize total utility (utilitarianism and prioritarianism), whereas personalized pricing can ethically outperform unitary pricing under social welfare functions that tend to prioritize equality (egalitarianism and leximin). Our findings illustrate various intricacies and dynamics regarding the circumstances under which personalized pricing can be considered ethical.

Supervised Sentiment Analysis of COVID-19 Tweets: Insights into Public Reactions and Trends

In the modern digital era, social media platforms have experienced exponential growth, serving as a medium for individuals to share their emotions and opinions. Twitter, a prominent social media platform, hosts a substantial number of users who regularly express their thoughts through tweets. This research paper focuses on conducting sentiment analysis on tweets pertaining to the COVID-19 situation in India. By utilizing a lexicon-based approach using TextBlob, we determine sentiment scores for each tweet. These scores are then used to categorize the tweets into Positive, Negative, or Neutral sentiment categories. To further enhance the accuracy of sentiment classification, we employ the Naïve Bayes machine learning model, achieving an impressive accuracy rate of 87.65%. The sentiment analysis results obtained through this study provide valuable insights for governmental bodies and other organizations, enabling them to formulate effective policies in order to address similar issues in the future.

Sentiment Analysis of Hate Speech against DPR-RI on Twitter Using Naive Bayes and KNN Algorithms

According to surveys, the rise in social media users has resulted in an increase of hate speech, and Twitter is one of the most popular platforms for this type of speech. The tweet feature on Twitter enables users to make repeated instances of hate speech, making Twitter data very intriguing to analyze. This study aims to investigate whether a tweet contains hate speech towards the Indonesian House of Representatives (DPR-RI). The research employed crawling techniques to gather data from Twitter using the Twitter API feature. The Naïve Bayes algorithm was applied, and the results were compared with the accuracy of the K-Nearest Neighbor. After preprocessing, the total data obtained was 1,494, with 956 test data and 538 training data. The study revealed that Twitter users' sentiment towards DPR-RI was 49.2% positive and 50.8% negative sentiment when tested using Naïve Bayes. Meanwhile, KNN showed 23.4% positive and 76.6% negative sentiment. The high negative sentiment in both classifiers suggests that Twitter users frequently express hate speech towards DPR-RI. Naïve Bayes algorithm showed the highest prediction accuracy at 98.32%, while the K-Nearest Neighbor algorithm had an accuracy of only 62.84%.

Sentiment Analysis Towards the KitaLulus Application Using the Naive Bayes Method from Google Play Store Reviews

Job search apps like KitaLulus are essential in helping graduates find jobs based on their skills and interests. Sentiment analysis is needed to understand user opinions about the KitaLulus application. The Naive Bayes method is used in this analysis because of its high efficiency and accuracy. This research used 597 data and achieved an accuracy rate of 91%. The eval_uation results show positive sentiment values for precision, recall, and f1-score of 0.99, 0.94, and 0.97 respectively. On the other hand, the model performance is low for negative and neutral sentiments. The aim of this research is to increase user understanding of the KitaLulus application and provide valuable assistance to developers in their efforts to improve the quality of the application.

Lexicon-Based Approach in Sentiment Analysis of Yemeni Dialect for Social Media Network

Recently, the number of Yemeni users has been expanding quickly on social media platforms. Most research in Arabic sentiment analysis has gained on Modern Standard Arabic (MSA) and some specific dialects, such as Egyptian, Levantine, and Gulf. However, there is a noticeable gap in Yemeni dialect sentiment analysis research. The reason for that is the lack of reliable Yemeni lexical and corpus and a real dataset for social media sentiment analysis. This research addresses this lack by presenting the Yemeni Dialect sentiment lexicon and corpus. This lexicon and corpus provide valuable resources for researchers and practitioners seeking to analyze sentiment in Yemeni dialect social media content, contributing to a better understanding of Yemeni public opinion, social media monitoring, marketing, cultural understanding, and assisting in efforts to respond to crises in Yemen. The Yemeni Dialect sentiment lexicon is enriched with a reasonable number of words and phrases categorized according to their positive and negative sentiment tendencies. Moreover, we constructed a corpus dataset of more than 54,000 comments built from the Facebook platform. A large dataset of unlabeled comments from the main Yemeni telecommunications companies in Yemen (Yemen Telecom, Yemen Mobile, YOU, and Sabafon), are people commenting on a public issue related to the services provided by those companies. The lexicon-based approach is used to extract the sentiment’s polarity and label each of the provided comments to formulate a corpus dataset as being either positive, negative, or neutral. The evaluation metrics of experiments are accuracy, recall, precision, f-measure, and the confusion matrix. The accuracy result of the lexicon-based labeling approach was calculated through a comparison between the achieved results and the ones achieved through manually labeled comments by three Yemeni experts. Evaluation results using a lexicon-based approach achieved an accuracy of 90.05%.

Can Negative Emotions on Social Media Predict Corporate Financial Risks: Perspective from Baidu Tieba

The intrinsic relationship between social media and corporate development has emerged as one of the key research topics today. Most current studies focus on the positive impacts and opportunities that social media can bring to enterprises. However, there remains a lack of a unified explanation for the influence of negative news on social media on businesses. Consequently, the research theme of this paper is to explore whether negative sentiments on social media can predict corporate financial risks. Firstly, this paper collects data related to bearish posts on social media and financial risks of enterprises. Subsequently, regression analysis is conducted on the data using SPSS. The results reveal that, under the condition of controlling the asset-liability ratio, negative posts on social media do have an impact on corporate financial risks, with a greater number of negative posts correlating to a higher level of financial risk for the enterprise. This paper fills a gap in understanding the negative implications of social media on certain aspects of businesses and provides data support for researching the influence of negative sentiments on social media on corporate financial risks.