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This study aims to develop a conceptual model of positive employee experience using sentiment analysis within algorithm-based human resource (HR) strategies. Its goal is to enhance HR professionals’ understanding of employee experiences and enable data-driven decision-making to create a positive work environment, thereby contributing to the originality of HR research.

The study conducts sentiment analysis – a text mining technique – to assess employee reviews and extract distinct positive experience factors. The employed data-driven methodology serves to fortify the reliability and objectivity of the analysis, ultimately resulting in a more refined depiction of the conveyed sentiment.

Utilizing sentiment analysis, the authors identified 135 keywords that signify positive employee experiences. These keywords were then categorized into four clusters aligned with factors influencing employee experience: work, relationships, organizational system and organizational culture, employing an inductive approach. The framework outlines the process of nurturing positive employee experiences throughout the employee life cycle, incorporating insights from the affective events theory and cognitive appraisal theory.

Data-driven insights empower HR professionals to enhance employee satisfaction, engagement and productivity. HR managers implementing AI-assisted HR ecosystems need digital and data science skills. Additionally, these insights can offer practical support in accentuating diversity and ethical considerations within the organizational culture. Candid employee data can enhance leadership and support diversity in organizational culture. Managers play a crucial communication role, ensuring flexible access to personalized HR solutions.

Applying sentiment analysis through opinion mining allows for the collection of unstructured data, reflecting authentic employee perceptions. This innovative approach expedites issue identification and targeted actions, enhancing employee satisfaction. Textual reviews, integral to employee feedback, offer comprehensive insights. Additionally, considering subjectivity and review length in online employee reviews adds value to understanding experiences (Zhao et al., 2019). This study surpasses prior research by directly identifying key factors of employee experience through the analysis of actual employee review texts, addressing a gap in understanding beyond previous attempts.

General Data Protection Regulation (GDPR) of the European Union (EU) was passed to protect data privacy. Though the GDPR intended to address issues related to data privacy in the EU, it created an extra-territorial effect through Articles 3, 45 and 46. Extra-territorial effect refers to the application or the effect of local laws and regulations in another country. Lawmakers around the globe passed or intensified their efforts to pass laws to have personal data privacy covered so that they meet the adequacy requirement under Articles 45–46 of GDPR while providing comprehensive legislation locally. This study aims to analyze the Malaysian and Saudi Arabian legislation on health data privacy and their adequacy in meeting GDPR data privacy protection requirements.

The research used a systematic literature review, legal content analysis and comparative analysis to critically analyze the health data protection in Malaysia and Saudi Arabia in comparison with GDPR and to see the adequacy of health data protection that could meet the requirement of EU data transfer requirement.

The finding suggested that the private sector is better regulated in Malaysia than the public sector. Saudi Arabia has some general laws to cover health data privacy in both public and private sector organizations until the newly passed data protection law is implemented in 2024. The finding also suggested that the Personal Data Protection Act 2010 of Malaysia and the Personal Data Protection Law 2022 of Saudi Arabia could be considered “adequate” under GDPR.

The research would be able to identify the key principles that could identify the adequacy of the laws about health data in Malaysia and Saudi Arabia as there is a dearth of literature in this area. This will help to propose suggestions to improve the laws concerning health data protection so that various stakeholders can benefit from it.

Kerala, a southern state in India, is acknowledged for its socio-economic reforms such as quality health care, gender parity, high literacy rate and more. However, recent trends show that the state has the highest incidence of various noncommunicable diseases in the country, including diabetes, hypertension and heart coronary artery disease. This research paper aims to examine the link between the Kerala population’s lifestyle, diet and genetic factors and its correlation with a heightened cardio-metabolic risk.

Using Dixon Wood’s interpretive synthesis, this qualitative literature review is systematically used by searching, gathering articles, theme building, comparing and criticising the evidence.

The result shows that only minimal evidence is available regarding the genetic makeup of the Kerala community, food patterns and its link to the high prevalence of non-communicable diseases (NCDs). However, limited and contradicting evidence and studies restricted to a particular region in the state demand more research on this domain.

It is vital to review the diet habits of Keralites due to the alarmingly high prevalence of NCDs. To the best of the authors’ knowledge, this is the first comprehensive review of the diet habits of Kerala and their link to NCDs.

Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models.