Search Results (308)

In the sentiment analysis of online comments, all comments are generally considered as a whole, with little attention paid to the inevitable emotional fluctuations in comments caused by changes in product value. In this study, we analyzed the online comments related to apple sales on Chinese e-commerce platforms, and combined topic models, sentiment analysis, and transfer learning to investigate the impact of product value on emotional fluctuations in online comments. We found that as product value changes, the sentiment of online comments undergoes significant fluctuations. Among the prominent negative sentiments, the proportion of topics influenced by product value significantly increases as product value decreases. This study reveals the correlation between changes in product value and sentiment fluctuations in online comments, and demonstrates the necessity of classifying online comments based on product value as an indicator. This study offers a novel perspective for enhancing sentiment analysis by incorporating product value dynamics. Full article

This study investigates public perceptions of sustainable mobility within university environments, which are important trip generation hubs with the potential to influence and disseminate sustainable mobility behaviors. Using sentiment analysis on 120,236 tweets from São Paulo, Rio de Janeiro, Lisbon, and Porto, tweets were classified into positive, neutral, and negative sentiments to assess perceptions across transport modes. It was hypothesized that universities would exhibit more positive sentiment toward active and public transport modes compared to perceptions of these modes within the broader city environment. Results show that active modes and public transport consistently receive higher positive sentiment rates than individual motorized modes, and, considering the analyzed contexts, universities demonstrate either similar (São Paulo) or more positive perceptions compared to the overall sentiment observed in the city (Rio de Janeiro, Lisbon, and Porto). Chi-square tests confirmed significant associations between transport mode and sentiment distribution. An exploratory analysis using topic modeling revealed that perceptions around bicycle use are linked to themes of safety, cycling infrastructure, and bike sharing. The findings highlight opportunities to promote sustainable mobility in universities by leveraging user sentiment while acknowledging limitations such as demographic bias in social media data and potential misclassification. This study advances data-driven methods to support targeted strategies for increasing active and public transport in university settings. Full article

This study investigates the relationship between overconfidence and meme stock valuation, drawing on panel data from 28 meme stocks listed from 2019 to 2024. The analysis incorporates key financial indicators, including Tobin’s Q ratio, market capitalization, return on assets, leverage, and volatility. A range of overconfidence proxies is employed, including changes in trading volume, turnover rate, changes in outstanding shares, and alternative measures of excessive trading. We observe a significant positive relationship between overconfidence (as measured by changes in trading volume) and firm valuation, suggesting that investor biases contribute to notable pricing distortions. Leverage has a significant negative relationship with firm valuation. In contrast, market capitalization has a significant positive relationship with firm valuation, implying that meme stock investors respond to both speculative sentiment and traditional firm fundamentals. Robustness checks using alternative proxies reveal that turnover rate and changes in the number of shares are negatively related to valuation. This shows the complex dynamics of meme stocks, where psychological factors intersect with firm-specific indicators. However, results from a dynamic panel model estimated using the Dynamic System Generalized Method of Moments (GMM) show that the turnover rate has a significantly positive relationship with firm valuation. These results offer valuable insights into the pricing behavior of meme stocks, revealing how investor sentiment impacts periodic valuation adjustments in speculative markets. Full article

Climate change presents serious challenges for infrastructure, regional planning, and public awareness. However, effectively understanding and analyzing large-scale climate discussions remains difficult. Traditional methods often struggle to extract meaningful insights from unstructured data sources, such as social media discourse, making it harder to track climate-related concerns and emerging trends. To address this gap, this study applies Natural Language Processing (NLP) techniques to analyze large volumes of climate-related data. By employing supervised and weak supervision methods, climate data are efficiently labeled to enable targeted analysis of regional- and infrastructure-specific climate impacts. Furthermore, BERT-based Named Entity Recognition (NER) is utilized to identify key climate-related terms, while sentiment analysis of platforms like Twitter provides valuable insights into trends in public opinion. AI-driven visualization tools, including predictive modeling and interactive mapping, are also integrated to enhance the accessibility and usability of the analyzed data. The research findings reveal significant patterns in climate-related discussions, supporting policymakers and planners in making more informed decisions. By combining AI-powered analytics with advanced visualization, the study enhances climate impact assessment and promotes the development of sustainable, resilient infrastructure. Overall, the results demonstrate the strong potential of AI-driven climate analysis to inform policy strategies and raise public awareness. Full article

This work explores the link between CEO turnover patterns and firms’ climate change exposure in a data set of over two thousand U.S. publicly traded firms. The findings demonstrate that CEO turnover is negatively associated with measures of climate change exposure developed with machine learning based on the frequency of discussions linked to climate change in the firms’ earnings conference calls. The results further indicate that this significant negative relationship exists in the year after the CEO’s departure from the firm, not before their departure. CEO turnover scenarios differ in their impact on a firm’s climate change exposure and sentiment. The focus of a firm’s management and financial analysts covering the firm can shift away from the issues of climate change. The negative and significant relationship with firms’ climate change exposure is observed particularly for forced CEO departures in firings or resignations, as well as for outsider CEO replacements. No significant relationship is found for CEO departures due to retirement or for cases of internal CEO succession. The results provide insights for decision makers, investors and boards of directors trying to evaluate the role of CEO turnover in climate change exposure at firms. Full article

Scenario analysis and the modeling of typhoons are fundamental prerequisites for effective emergency decision-making. However, current studies on typhoon scenario modeling lack analyses of cascading effects and users’ concerns, failing to represent cascading disaster impacts and user adaptability. This study constructs a scenario evolution model for typhoons and their cascading disasters through typhoon-related public opinion mining and an analysis of disaster evolution characteristics to address these limitations. Specifically, this study analyzes and extracts information about users’ sentiments and concerns based on public opinion data. Then, public opinion and typhoon evolution progression analyses are conducted, identifying cascading disaster evolution characteristics to determine scenario elements. The scenario model is constructed by calculating scenario node probability distributions using dynamic Bayesian networks (DBNs). In this study, Typhoon Bebinca is selected to verify the proposed scenario model; the results demonstrate that the model is reliable and its evolution process aligns with the impacts of typhoon cascading disasters. This study also reveals two critical insights: (1) Users’ concerns will change with typhoon evolution. (2) Emergency measures for dealing with typhoons and their cascading disasters are fragmented. It is essential to consider their cascading effects when enacting these measures. These findings provide novel insights that could aid government agencies in their decision making. Full article

With the rapid development of the Internet, the speed and scope of sudden public events disseminating in cyberspace have grown significantly. Current methods of quantifying public opinion heat often neglect emotion-driven factors and user interaction behaviors, making it difficult to accurately capture fluctuations during dissemination. To address these issues, first, this study addressed the complexity of interaction behaviors by introducing an approach that employs the information gain ratio as a weighting indicator to measure the “interaction heat” contributed by different interaction attributes during event evolution. Second, this study built on SnowNLP and expanded textual features to conduct in-depth sentiment mining of large-scale opinion texts, defining the variance of netizens’ emotional tendencies as an indicator of emotional fluctuations, thereby capturing “emotional heat”. We then integrated interactive behavior and emotional conflict assessment to achieve comprehensive heat index to quantification and dynamic evolution analysis of online public opinion heat. Subsequently, we used Hodrick–Prescott filter to separate long-term trends and short-term fluctuations, extract six key quantitative features (number of peaks, time of first peak, maximum amplitude, decay time, peak emotional conflict, and overall duration), and applied K-means clustering algorithm (K-means) to classify events into three propagation patterns, which are extreme burst, normal burst, and long-tail. Finally, this study conducted ablation experiments on critical external intervention nodes to quantify the distinct contribution of each intervention to the propagation trend by observing changes in the model’s goodness-of-fit ($R^2$) after removing different interventions. Through an empirical analysis of six representative public opinion events from 2024, this study verified the effectiveness of the proposed framework and uncovered critical characteristics of opinion dissemination, including explosiveness versus persistence, multi-round dissemination with recurring emotional fluctuations, and the interplay of multiple driving factors. Full article

Addressing climate change requires deeper insight into the psychological drivers of pro-environmental behavior. This study investigates how personality traits, climate-related emotions, and demographic factors can predict sustainable consumption and climate action participation using a Bayesian regression approach. Drawing from the HEXACO personality model and key emotional predictors—Climate Change Worry (CCW) and environmental empathy (EE)—we analyzed data from 604 adults in Greece to assess both private and public climate-related behaviors. This research is novel in its integrative approach, combining dispositional traits and affective states within a Bayesian analytical framework to simultaneously predict both sustainable consumption and climate action. Bayesian model testing highlighted education as the most powerful and reliable predictor of sustainable consumption, with increasing levels—namely Doctoral education—linked to more environmentally responsible action. CCW produced small but reliable effects, supporting hypotheses that moderate emotional concern will lead to sustainable behavior when linked to efficacy belief. The majority of HEXACO traits, e.g., Honesty–Humility and Conscientiousness, produced limited predictive power. This indicates in this case that structural and emotional considerations were stronger than dispositional personality traits. For climate action involvement, Bayesian logistic models found no considerable evidence of any predictor, corroborating the perspective that public participation in high effort action is most likely to rely on contextual enablers instead of internal sentiments or attributes. A significant interaction effect between education and gender also indicated that the sustainability effect of education is moderated by sociocultural identity. Methodologically, this research demonstrates the strengths of Bayesian analysis in sustainability science to make sensitive inference and model comparison possible. The results highlight the importance of affect-related structural variables in behavioral models and have applied implications for theory-informed and targeted climate education and communication interventions to enable different populations to act sustainably. Full article

In today’s hyperconnected world, disaster announcements—regardless of actual impact—can significantly shape consumer behaviour and brand perception in the hospitality sector. This study investigates how customers respond online to disaster-related signals, focusing on digital marketing activities by luxury hotels in Santorini, Greece. Drawing on a case study of the Santorini Earthquake in February 2025—during which the Greek government declared a state of emergency—we use big data analytics, including web traffic metrics, social media interaction and fuzzy cognitive mapping, to analyse behavioural shifts across platforms. The findings indicate that disaster signals trigger increased engagement, altered sentiment and changes in advertising efficiency. This study provides actionable recommendations for tourism destinations and hospitality brands on how to adapt digital strategies during crisis periods. Full article

Effective analysis of central bank communications is critical for anticipating monetary policy changes and guiding market expectations. However, traditional natural language processing models face significant challenges in processing lengthy and nuanced policy documents, which often exceed tens of thousands of tokens. This study addresses these challenges by proposing a novel integrated deep learning framework based on Hyena Hierarchy architectures, which utilize sub-quadratic convolution mechanisms to efficiently process ultra-long sequences. The framework employs Delta-LoRA (low-rank adaptation) for parameter-efficient fine-tuning, updating less than 1% of the total parameters without additional inference overhead. To ensure robust performance across institutions and policy cycles, domain-adversarial neural networks are incorporated to learn domain-invariant representations, and a multi-task learning approach integrates auxiliary hawkish/dovish sentiment signals. Evaluations conducted on a comprehensive dataset comprising Federal Open Market Committee statements and European Central Bank speeches from 1977 to 2024 demonstrate state-of-the-art performance, achieving over 6% improvement in macro-F1 score compared to baseline models while significantly reducing inference latency by 65%. This work offers a powerful and efficient new paradigm for handling ultra-long financial policy texts and demonstrates the effectiveness of integrating advanced sequence modeling, efficient fine-tuning, and domain adaptation techniques for extracting timely economic signals, with the aim to open new avenues for quantitative policy analysis and financial market forecasting. Full article

Multimodal sentiment analysis aims to uncover human affective states by integrating data from multiple sensory sources. However, previous studies have focused on optimizing model architecture, neglecting the impact of objective function settings on model performance. Given this, this study introduces a new framework, DMMSA, which utilizes the intrinsic correlation of sentiment signals and enhances the model’s understanding of complex sentiments. DMMSA incorporates coarse-grained sentiment analysis to reduce task complexity. Meanwhile, it embeds a contrastive learning mechanism within the modality, which decomposes unimodal features into similar and dissimilar ones, thus allowing for the simultaneous consideration of both unimodal and multimodal emotions. We tested DMMSA on the CH-SIMS, MOSI, and MOEI datasets. When only changing the optimization objectives, DMMSA achieved accuracy gains of 3.2%, 1.57%, and 1.95% over the baseline in five-class and seven-class classification tasks. In regression tasks, DMMSA reduced the Mean Absolute Error (MAE) by 1.46%, 1.5%, and 2.8% compared to the baseline. Full article

With the rapid advancement of information technology and the increasing maturity of online shopping platforms, cross-border shopping has experienced rapid growth. Online consumer reviews, as an essential part of the online shopping process, have become a vital way for merchants to obtain user feedback and gain insights into market demands. The research employs Python tools (Jupyter Notebook 7.0.8) to analyze the 14,078 pieces of review text data from the top four best-selling products in a certain product category on a certain cross-border e-commerce platform. By applying social network analysis, constructing LDA (Latent Dirichlet Allocation) topic models, and establishing LSTM (Long Short-Term Memory) sentiment classification models, the topics and sentiment distribution of the review set are obtained, and the evolution trends of topics and sentiments are analyzed according to different periods. The research finds that in the overall review set, consumers’ focus is concentrated on five aspects: functional features, quality and cost-effectiveness, usage effectiveness, post-purchase support, and design and assembly. In terms of changes in review sentiments, the negative proportion of the topics of functional features and usage effects is still relatively high. Given the above, this study integrates the 4P and 4C theories to propose strategies for enhancing the marketing capabilities of cross-border e-commerce in the context of digital cross-border operations, providing theoretical and practical marketing insights for cross-border e-commerce enterprises. Full article

During crises, people use X to share real-time updates. These posts reveal public sentiment and evolving emergency situations. However, the changing sentiment in tweets coupled with anomalous patterns may indicate significant events, misinformation or emerging hazards that require timely detection. By using a neural network, and employing deep learning techniques for crisis observation, this study proposes a pipeline for sentiment analysis and anomaly detection in crisis-related tweets. The authors used pre-trained BERT to classify tweet sentiment. For sentiment anomaly detection, autoencoders and recurrent neural networks (RNNs) with an attention mechanism were applied to capture sequential relationships and identify irregular sentiment patterns that deviate from standard crisis talk. Experimental results show that neural networks are more accurate than traditional machine learning methods for both sentiment categorization and anomaly detection tasks, with higher precision and recall for identifying sentiment shifts in the public. This study indicates that neural networks can be used for crisis management and the early detection of significant sentiment anomalies. This could be beneficial to emergency responders and policymakers and support data-driven decisions. Full article

With rising urgency around carbon emissions and climate change, electrification has emerged as a central focus in traditionally combustion-reliant industries. With increasing regulatory restrictions on automotive and smaller off-highway markets (<25 hp), the heavy equipment industry faces growing pressures to adopt hybrid and fully electric solutions. Current literature primarily addresses technical electrification challenges, leaving a gap in understanding industry perspectives. This study explores trends, challenges, and expectations of electrification from industry representatives’ viewpoints, using data from 84 surveys conducted at the CONEXPO/CONAGG trade show and sentiment analysis of 100 interview notes gathered through an NSF Innovation Corps workshop. Results indicate substantial uncertainty toward electrification, with key limitations including power-to-weight ratios, high costs, maintenance, leakage concerns, and reliability of electronic components. The majority (77%) preferred traditional hydraulic systems due to familiarity and reliability, though concerns over maintenance and environmental impact remain prevalent. Participants anticipate a gradual industry transition, projecting widespread adoption of hybrid solutions in 10–15 years and longer timelines for fully electric systems. Effective adoption of greener technologies is likely through industry-wide standards and financial incentives. This study emphasizes the industry’s cautious yet gradually increasing openness to electrification amidst persistent technological and economic challenges. Full article

This study explores the role of social media in raising awareness, fostering community engagement, and supporting efforts to reduce marine and coastal litter in Cyprus. Focusing on campaigns led by community-based organizations such as Yesil Baris Hareketi, Teneke Cocuk, and the Spot Turtle Project, the research highlights how digital platforms can mobilize public opinion and influence pro-environmental behaviors. Artificial intelligence (AI) algorithms particularly natural language processing (NLP) techniques were employed to analyze large volumes of social media data, enabling the detection of engagement patterns, sentiment dynamics, and thematic trends within environmental campaigns. A mixed-methods approach was adopted, combining social media content analysis, engagement metrics, and stakeholder interviews to provide a comprehensive view of the digital advocacy landscape. Findings suggest that social media plays a critical role in shaping public perceptions of marine litter; however, sustaining long-term behavioral change remains a significant challenge. The study offers practical recommendations for enhancing digital strategies, strengthening stakeholder collaboration, and integrating social media efforts with policy development and environmental education. Full article