Purpose Electronic word of mouth in the form of user-generated content (UGC) in social media plays an important role in influencing customer decision-making and enhancing service providers’ brand images, sales and service innovations. While few research studies have explored real content generated by hotel guests in social media, business analytics techniques are still not widely seen in the literature and how such techniques can be deployed to benefit hoteliers has not been fully explored. Thus, this study aims to explore the significant factors that affect hotel guest satisfaction via UGC and business analytics and also to showcase the use of business analytics tools for both the hospitality industry and the academic world. Design/methodology/approach This study uses big data and business analytics techniques. Big data and business analytics enable hoteliers to develop effective and efficient strategies improving products and services for guest satisfaction. Therefore, this study analyzes 200,431 hotel reviews on Tripadvisor.com through business analytics to explore and assess the significant factors affecting guest satisfaction. Findings The findings show that service, room and value evaluations are the top-three factors affecting overall guests’ satisfaction. While brand type and negative emotions are negatively associated with guests’ satisfaction, all other factors considered were positively associated with guests’ satisfaction. Originality/value The current study serves as a great starting point to further explore the relationship between specific evaluation factors and guests’ overall satisfaction by analyzing user-generated online reviews through business analytics so as to assist hoteliers to resolve performance-related problems by analyzing service gaps that exist in these influential factors.
The COVID-19 pandemic, an unprecedented public health crisis in the 21 st century, demonstrates how risks are unequally distributed among different socioeconomic groups, how bureaucratic systems can be ineffectively adapting to the fast-paced changing dynamics, and how multi-level governance frameworks may lack coordination between national and regional sectors. The pandemic does not cause the unjust prerogatives to quarantine, testing kits, and even potential vaccines. The injustice, ineffectiveness, and a lack of coordination existed before the pandemic and are more noticeably presented by this public health disaster. No hazards are 'natural'. The coronavirus may originate from nature, but the adverse effects are mostly generated and magnified by current socioeconomic orders. The elderly (Armitage & Nellums, 2020), gender-based violence victims (Chandan et al., 2020), people living with disabilities (Pereira-Sanchez et al., 2020), immigrants (Keller & Wagner, 2020), and children in poverty (Lancker & Parolin, 2020) are more exposed to the increased morbidity and mortality. Globally, low-income and middle-income countries are at higher risks of the pandemic's negative effects, worsening existing structural inequalities, such as food security (Health, 2020; Hopman, Allegranzi, & Mehtar, 2020; Kelley et al., 2020). Amid the COVID-19 pandemic, when uncertainty becomes the new normal, the notion of resilience has frequently been mentioned among researchers, policymakers, and practitioners from various fields, including public health, urban planning, emergency management, and economics. Is such renaissance of resilience useful in the face of the pandemic? ANOTHER BUZZWORD OR THE NEW INSIGHT? In the past few decades, there have been incoherent definitions, interpretations, and analytical models proposed to understand, apply, and assess the notion of resilience and its related concepts (
This paper introduces KSW, a Khmer-specific approach to keyword extraction that leverages a specialized stop word dictionary. Due to the limited availability of natural language processing resources for the Khmer language, effective keyword extraction has been a significant challenge. KSW addresses this by developing a tailored stop word dictionary and implementing a preprocessing methodology to remove stop words, thereby enhancing the extraction of meaningful keywords. Our experiments demonstrate that KSW achieves substantial improvements in accuracy and relevance compared to previous methods, highlighting its potential to advance Khmer text processing and information retrieval. The KSW resources, including the stop word dictionary, are available at the following GitHub repository: (https://github.com/back-kh/KSWv2-Khmer-Stop-Word-based-Dictionary-for-Keyword-Extraction.git).
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