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This paper purposed a multi-facet sentiment analysis system.

Hence, This paper uses multidomain resources to build a sentiment analysis system. The manual lexicon based features that are extracted from the resources are fed into a machine learning classifier to compare their performance afterward. The manual lexicon is replaced with a custom BOW to deal with its time consuming construction. To help the system run faster and make the model interpretable, this will be performed by employing different existing and custom approaches such as term occurrence, information gain, principal component analysis, semantic clustering, and POS tagging filters.

The proposed system featured by lexicon extraction automation and characteristics size optimization proved its efficiency when applied to multidomain and benchmark datasets by reaching 93.59% accuracy which makes it competitive to the state-of-the-art systems.

The construction of a custom BOW. Optimizing features based on existing and custom feature selection and clustering approaches.

Using intersectionality and introducing newer developments from critical cross-cultural management studies, this paper aims to discuss how diversity is applicable to changing cultural contexts.

The paper is a conceptual paper built upon relevant empirical research findings from critical cross-cultural management studies.

By applying intersectionality as a conceptual lens, this paper underscores the practical and conceptual limitations of the business case for diversity, in particular in a culturally diverse international business (IB) setting. Introducing newer developments from critical cross-cultural management studies, the authors identify the need to investigate and manage diversity across distinct categories, and as intersecting with culture, context and power.

This paper builds on previous empirical research in critical cross-cultural management studies using intersectionality as a conceptual lens and draws implications for diversity management in an IB setting from there. The authors add to the critique of the business case by showing its failures of identifying and, consequently, managing diversity, equality/equity and inclusion (DEI) in IB settings.

Organizations (e.g. MNEs) are enabled to clearly see the limitations of the business case and provided with a conceptual lens for addressing DEI issues in a more contextualized and intersectional manner.

This paper introduces intersectionality, as discussed and applied in critical cross-cultural management studies, as a conceptual lens for outlining the limitations of the business case for diversity and for promoting DEI in an IB setting in more complicated, realistic and relevant ways.

Robots have been adopted in numerous tourism and hospitality sectors, including restaurants. This study aims to investigate fast-food employees' use of service robots (SRs) in Pakistan.

This study used a conceptual model based on innovation resistance theory (IRT). By employing structural equation modeling (SEM) in Smart-PLS 3.2.8, we evaluated data from 247 valid respondents.

The findings demonstrated that drivers of robot adaptation significantly influenced image barriers, risk barriers, traditional barriers, usage barriers and value barriers. The results also revealed that usage, image and traditional barriers significantly affect usage intention.

This study enhances the research on robotics acceptance in tourism and hospitality and subsequently aids in the planning for post-COVID-19 resumption. This study offers several practical and theoretical insights for further investigation.

This pan-Canadian study examines the information practices of STEM-trained immigrant women to Canada as they navigate workfinding and workplace integration. Our study focuses on a population of highly skilled immigrant women from across Canada and uses an information practice lens to examine their lived experiences of migration and labour market integration. As highly trained STEM professionals in pursuit of employment, our participants have specific needs and challenges, and as we explore these, we consider the intersection of their information practices with government policies, settlement services and the hiring practices of STEM employers.

We conducted a qualitative study using in-depth interviews with 74 immigrant women across 13 Canadian provinces and territories to understand the nature of their engagement with employment-seeking in STEM sectors. This article reports the findings related to the settlement and information experiences of the immigrant women as they navigate new information landscapes.

As immigrants, as women and as STEM professionals, the experiences of the 74 participants reflect both marginality and privilege. The reality of their intersectional identities is that these women may not be well-served by broader settlement resources targeting newcomers, but neither are the specific conventions of networking and job-seeking in the STEM sectors in Canada fully apparent or accessible to them. The findings also point to the broader systemic and contextual factors that participants have to navigate and that shape in a major way their workfinding journeys.

The findings of this pan-Canadian study have theoretical and practical implications for policy and research. Through interviews with these STEM professionals, we highlight the barriers and challenges of an under-studied category of migrants (the highly skilled and “desirable” type of immigrants). We provide a critical discussion of their settlement experiences and expose the idiosyncrasies of a system that claims to value skilled talent while structurally making it very difficult to deliver on its promises to recruit and retain highly qualified personnel. Our findings point to specific aspects of these skilled professionals’ experiences, as well as the broader systemic and contextual factors that shape their workfinding journey.

This research was developed to identify artificial intelligence (AI) opportunities and challenges in higher education.

This qualitative research was developed using the six-step thematic analysis method (Braun and Clark, 2006). Participants in this study were AI PhD students from Tehran University in 2022–2023. Purposive sampling was used to select the participants; a total of 15 AI PhD students, who were experts in this field, were selected and interviews were conducted.

The authors considered the opportunities that AI creates for higher education in eight secondary subthemes (for faculty members, for students, in the teaching and learning process, for assessment, the development of educational structures, the development of research structures, the development of management structures and the development of academic culture). Correspondingly, The authors identified and categorized the challenges that AI creates for higher education.

Concerning the intended research, several limitations are significant. First, the statistical population was limited, and only people with characteristics such as being PhD students, studying at Tehran University and being experts in AI could be considered the statistical population. Second, caution should be exercised when generalizing the results due to the limited statistical population (PhD students from Tehran University). Third, the problem of accessing some students due to their participation in research grants, academic immigration, etc.

The innovation of the current research is that the authors identified the opportunities and challenges that AI creates for higher education at different levels. The findings of this study also contribute to the enrichment of existing knowledge in the field regarding the effects of AI on the future of higher education, as researchers need more understanding of AI developments in the future of higher education.

The fundamental relationship between art and resilience is striking in this passage and in the reflections shared by other artists. This paper aims to attempt to piece together the fragmented and insecure realities in South Sudan through the lens of different artists. The paper argues that focusing on art is an important way into a deeper more nuanced picture of how women and men find and maintain resilience in humanitarian contexts.

The data is qualitatively collected through an innovative art-based creative method known as story circles. The circles consisted of artists who shared what their art form meant to them.

The picture that emerges contrasts starkly against the dark narratives that commonly portray South Sudan. Art making spaces and the outputs that come from them are cultural resources often overlooked by humanitarian stakeholders and yet, as the authors show, hold the potential to support more locally rooted and responsive approaches to resilience building.

Very little research has been conducted on the ways in which people in South Sudan draw on and find resilience in art and art making.

This study aims to examine the tourists' visit intention by watching deepfake destination videos, using Information Manipulation and Media Richness Theory.

This study conducted a primary survey utilizing a structured questionnaire. In total, 1,360 tourists were surveyed, and quantitative data analysis was done using PLS-SEM.

The results indicate that the factors that affect the tourists' visit intention after watching deepfake videos include information manipulation tactics, trust and media richness. This study also found that perceived deception and cognitive load do not influence the tourists' visit intention.

The originality/salience of this study lies in the fact that this is possibly among the first to combine the Media Richness Theory and Information Manipulation for understanding tourists' visit intention and post-viewing deepfake destination videos.

The purpose of this paper is to critically examine the National Home Grown School Feeding Programme (NHGSFP) in Nigeria. Its special focus is to investigate the impact of NHGSFP on rural communities in Nigeria.

This paper adopts a survey research technique, aimed at gathering information from a representative sample of the population, as it is essentially cross-sectional, describing and interpreting the current situation. A total of 2,400 households were sampled across the six geopolitical regions of Nigeria.

The results from the use of a combined propensity score matching and logit model indicate that NHGSFP makes significant contributions to improving the health and educational status of rural school children, stimulates job creation and boosts rural economy.

This implies that a well-designed and integrated Home Grown School Feeding Programme (HGSFP) can make significant contributions to improving food security at the household level, spurring job creation and boosting agricultural markets.

This suggests the need for a purposeful engagement and support from all stakeholders to ensure the success of HGSFP.

This research adds to the literature on school feeding in low-income countries. It concludes that school feeding programmes have been shown to directly increase the educational and nutritional status of recipient children and indirectly impact the economic and social lives of themselves and their family.

While many works have reported adopting exploratory practice (EP) principles in language teaching research, only a few studies have explored the enactment of EP in an online extensive reading of students majoring in English education. Given the relative paucity of attention to the use of EP as the practitioner research in English language teaching (ELT), the present EP investigates how students understand online extensive reading practice mediated by online group discussion and extensive reading logs, where the first author served as the online extensive reading practice instructor.

The exploratory practice focuses on incorporating research into pedagogy and fastens the importance of the quality-of-life in the classroom. The data were collected through students reading logs and semi-structured interviews. The collected data were analyzed using the thematic analysis. In this case, there were six phases including familiarizing with the data, generating initial codes, searching for the themes, reviewing the themes, defining the theme and writing up.

The findings reveal that online group work driven by EP enables everybody to engage in learning activities. EP assists the students in perceiving their potential and gaining a better awareness of the need to devote themselves to the class. In the EP activities, they work together to build a peaceful situation to advance the quality of learning in EFL classrooms.

The present study’s limitation is the small sample. Apart from that, the research results cannot be generalized to other places.

This study suggests that EP is suitable to create a mutual understanding among the learners and teachers. To conclude, English language competency can be achieved in a pleasant atmosphere through EP.

The present study succeeded in adding new literature studies related to EPs by discussing online group discussions and their challenges during the learning process. These aspects were identified through reading logs and interviews with students. Thus, it focuses on the implementation and challenges of online group discussions.

Genre classification of songs based on lyrics is a challenging task even for humans, however, state-of-the-art natural language processing has recently offered advanced solutions to this task. The purpose of this study is to advance the understanding and application of natural language processing and deep learning in the domain of music genre classification, while also contributing to the broader themes of global knowledge and communication, and sustainable preservation of cultural heritage.

The main contribution of this study is the development and evaluation of various machine and deep learning models for song genre classification. Additionally, we investigated the effect of different word embeddings, including Global Vectors for Word Representation (GloVe) and Word2Vec, on the classification performance. The tested models range from benchmarks such as logistic regression, support vector machine and random forest, to more complex neural network architectures and transformer-based models, such as recurrent neural network, long short-term memory, bidirectional long short-term memory and bidirectional encoder representations from transformers (BERT).

The authors conducted experiments on both English and multilingual data sets for genre classification. The results show that the BERT model achieved the best accuracy on the English data set, whereas cross-lingual language model pretraining based on RoBERTa (XLM-RoBERTa) performed the best on the multilingual data set. This study found that songs in the metal genre were the most accurately labeled, as their text style and topics were the most distinct from other genres. On the contrary, songs from the pop and rock genres were more challenging to differentiate. This study also compared the impact of different word embeddings on the classification task and found that models with GloVe word embeddings outperformed Word2Vec and the learning embedding layer.

This study presents the implementation, testing and comparison of various machine and deep learning models for genre classification. The results demonstrate that transformer models, including BERT, robustly optimized BERT pretraining approach, distilled bidirectional encoder representations from transformers, bidirectional and auto-regressive transformers and XLM-RoBERTa, outperformed other models.