Sentiment analysis for social media and online document has been a burgeoning area in text mining for the last decade. However, Email sentiment analysis has not been studied and examined thoroughly even though it is one of the most ubiquitous means of communication. In this research, a hybrid sentiment analysis framework for Email data using term frequency-inverse document frequency term weighting model for feature extraction, and k-means labeling combined with support vector machine classifier for sentiment classification is proposed. Empirical results indicate comparatively better classification results with the proposed framework than other combinations.

Sentiment analysis for online text documents has been a burgeoning field of text mining among researchers and scholars for the past few decades. Nevertheless, sentiment analysis on large Email data, a ubiquity means of social networking and communication, has not been studied thoroughly. This paper proposes a framework for Email sentiment analysis using a hybrid scheme of algorithms combined with Kmeans clustering and support vector machine classifier. The evaluation for the framework is conducted through the comparison among three labeling methods, including SentiWordNet labeling, Kmeans labeling, and Polarity labeling, and five classifiers, including Support Vector Machine, Naïve Bayes, Logistic Regression, Decision Tree and OneR. Empirical results indicate a relatively high classification accuracy with proposed framework in comparison with other approaches.

Creativity is increasingly recognized as an essential 21st-century skill, critical for innovation, problem-solving, and personal growth. Educational systems have responded by prioritizing creative thinking, prompting researchers to explore the potential of Learning Analytics (LA) to support and enhance creativity. This systematic review synthesizes empirical studies, theoretical frameworks, and methodological innovations from databases such as Web of Science, Scopus, ERIC, ProQuest, and Google Scholar, examining how creativity is operationalized within LA contexts. The review identifies diverse assessment frameworks, encompassing divergent thinking tests, product-based evaluations, behavioral metrics, and process-oriented assessments, often underpinned by the “4 Ps of Creativity” framework (Person, Process, Product, Press). Tools such as automated scoring systems, multimodal analytics, and AI-enhanced assessments demonstrate the potential to objectively and reliably capture creative processes and outcomes. However, significant challenges remain, including definitional ambiguity, inconsistent metrics, scalability issues, and ethical concerns related to data privacy. This review underscores the transformative capacity of LA to foster creativity in education while highlighting the critical need for standardized, robust methodologies and inclusive frameworks. By addressing identified gaps, future research can advance innovative approaches to assess and cultivate creativity using LA.

A SEIQR-V epidemic model, including the exposure period, is established based on cellular automata. Considerations are made for individual mobility and heterogeneity while introducing measures of vaccinating susceptible populations and quarantining infectious populations. Referencing the random walk cellular automata and extended Moore neighborhood theories, influenza A(H1N1) is used as example to create a dynamic simulation using Matlab software. The simulated results match real data released by the World Health Organization, indicating the model is valid and effective. On this basis, the effects of vaccination proportion and quarantine intensity on epidemic propagation are analogue simulated, obtaining their trends of influence and optimal control strategies are suggested.