Search Results (58)

The rapid expansion of market-driven software product development has led to the increasing use of User-Generated Content (UGC), such as mobile application user reviews, as a valuable source of requirements. However, unlike the traditional requirements engineering (RE) process, data-driven RE introduces several challenges, particularly in requirements elicitation and prioritization. Traditional requirements prioritization techniques typically rely on stakeholders’ involvement; however, in data-driven and market-driven development contexts, explicit stakeholders are often absent. Thus, we propose a DAta-driven Requirements Prioritization (DARP) framework that integrates Natural Language Processing (NLP), topic modeling, and Large Language Models (LLMs) to automate requirements prioritization in a data-driven development context. The proposed framework utilizes BERTopic to identify latent topics in user reviews and incorporates Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to group semantically related requirements. The proposed framework introduces a robust and automated prioritization applied to mobile app reviews. The scope of the proposed framework is user-perspective prioritization. Our objective is to detect insights from app reviews to reflect the voice of the customer. The results indicate that leveraging NLP and topic modeling techniques provides an effective data-driven approach to requirements prioritization. Full article

With increasing demand for wellness tourism, identifying the key factors influencing emotional perceptions is essential for optimizing destination planning and management. Although Anhui Province has experienced rapid growth in wellness tourism destinations in recent years, scientific understanding of tourists’ emotional perceptions and their driving mechanisms has lagged behind this rapid expansion, a gap that can be addressed by integrating big data with spatial analysis to provide a scientific perspective for optimizing destination planning and informing regional wellness tourism policy. To address this gap, this study conducts a sentiment analysis of wellness bases in Anhui Province using user-generated content (UGC) data. Sentiment scores were quantified via SnowNLP, while kernel density, time-series, and multivariate statistical analyses were applied to examine spatial distributions, temporal dynamics of sentiments and review volumes, and emotional driving factors. The results indicate a spatial pattern of higher density in the south, lower density in the north, and dual-core agglomeration, closely linked to natural resource endowments. Temporally, sentiment scores rise in spring and summer and decline in winter, while review volumes peak in spring and autumn. Overall regression analyses reveal a significant positive effect of green coverage and a negative effect of accommodation prices. In the typological analysis, sentiment scores of Forest Wellness Bases (FWBs) relate to green coverage and negative ions, while Hydrological Wellness Bases (HWBs), Traditional Chinese Medicine Wellness Bases (TCMWBs), and Wellness Towns (WTs) are driven by the combined effects of facility services, locational price, and ecological environment. These findings provide a scientific basis for the sustainable development and differentiated management of wellness tourism destinations. Full article

In the era of e-commerce, consumers’ innovative insights are crucial for brands to improve their products and meet consumers’ potential needs. Although user-generated content (UGC) platforms have accumulated a vast amount of consumer voices, brands often face the dilemma of “rich data but scarce insights”: valuable consumer innovation insights are not only scarce but also expressed implicitly. Traditional methods have difficulty reliably identifying such insights in this task. Therefore, this study, for the first time, introduces a large language model (LLM)-augmented Siamese framework, aiming to improve the identification and generalization of consumer innovation insights in multi-platform UGC. Based on four Chinese social media platforms and a dataset of 133,538 comments, we conducted three experiments and performed a systematic comparison under five model configurations. The results show that our approach significantly outperforms traditional benchmarks and strong baselines in most experimental settings, while maintaining stable competitiveness in cross-platform tests. Finally, we showed how the identified innovation insights can be transformed into structured outputs for product planning, thereby facilitating the integration of consumer insights into the brand’s innovation process. Full article

The Jeju Olle Trail has evolved from a grassroots initiative into a contested space where post-pandemic growth intersects with environmental limits and fragmented governance. Moving beyond environment-centric models, this study examines the trail as a transcultural walking tourism system. The authors triangulated 900 user-generated content (UGC) narratives from major travel platforms (Korean, Chinese, and English) with semi-structured interviews from three key institutional informants (NTO, RTO, and NPO). The analysis explores how sustainable experiences are negotiated in practice. Findings suggest that Self-Determination Theory (SDT) constructs like autonomy are not universal constants but are culturally mediated through Western “digital detox,” Korean “collective healing,” and Chinese chūxīn (original heart) narratives. Institutional and narrative data indicate that these experiences appear linked to managing governance tensions between national mandates and localized stewardship. The study concludes that experiential sustainability involves navigating trade-offs regarding narratively signaled environmental impacts and community capacity. By framing walking tourism as a governance-dependent practice, this research demonstrates how culturally embedded mechanisms shape destination viability. Full article

With the rise in experience economy and the popularization of digital technology, user-generated content (UGC) has become a core data source for understanding tourist needs and evaluating the service quality of venues. As a landmark venue that combines science education, interactive experience, and landscape viewing, the service quality of the Macau Science Center directly affects tourists’ travel experience and word-of-mouth dissemination. However, existing studies mostly rely on traditional questionnaire surveys and lack multi-technology collaborative analysis. In order to accurately identify the factors affecting satisfaction, this study uses 788 valid UGC data from five major platforms, namely Google Maps reviews, TripAdvisor, Sina Weibo, Xiaohongshu (Rednote), and Ctrip, from January 2023 to November 2025. It integrates word frequency analysis, semantic network analysis, latent Dirichlet allocation (LDA) topic modeling, and Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment computing to construct a systematic research framework. The study found that (1) the core attention dimensions of users cover the needs of parent–child and family visits, exhibitions and interactive experiences, ticketing and consumption services, surrounding environment and landscape, emotional evaluation, and recommendation intention. (2) The keyword association network has gradually developed from a loose network in the early stage to a comprehensive experience-dense network. (3) LDA analysis identified five main potential demand themes: comprehensive visiting experience and scenario integration, parent–child interaction and characteristic scenario experience, core venue facilities and ticketing services, visiting value and emotional evaluation, and transportation and surrounding landscapes. (4) User emotions were predominantly positive, accounting for 82.7%, while negative emotions were concentrated in local service details, and the emotional scores showed a fluctuating upward trend. This study provides targeted suggestions for the service optimization of the Macau Science Center and also provides a methodological reference for UGC-driven research in similar cultural venues. Full article

The growth of e-commerce has led to a rapid increase in user-generated content (UGC), attracting scholars’ attention as a new data source for investigating customer requirements. However, existing requirements analysis methods fail to integrate three critical requirement indicators: stated importance, derived importance, and performance. Using only one or two of these indicators inevitably has its limitations. This paper proposes a novel framework for analyzing and prioritizing customer requirements based on multi-source UGC. First, customer requirements are extracted from online reviews and questions & answers using non-negative matrix factorization. Next, aspect-level sentiment analysis and multi-source data fusion are employed to calculate dual importance and performance. Specifically, we developed an improved importance–performance analysis (IPA) model, named dual importance–performance analysis (Du-IPA), which integrates the three indicators to classify requirement types in a 3D cube with corresponding improvement strategies. Finally, by combining the three indicators, an improved prospect value and PROMETHEE-II are proposed using prospect theory to prioritize CRs for product service improvement. The effectiveness of the proposed method is demonstrated through a case study of fresh food in online retail. Full article

Forest recreation is irreplaceable for the protection and sustainable development of urban environments. Understanding the structural characteristics of forest recreation networks in urban areas thus offers valuable theoretical and practical insights. Grounded in social network theory and spatial analysis of recreational behavior, this study leverages point of interest (POI) data for forest attractions, forest land cover data, and user-generated content (UGC) trajectory data to analyze the evolution of the forest recreation network in the Chang-Zhu-Tan Green Heart (CZTGH) of China—the world’s largest metropolitan ecological green heart area. Findings reveal that the forest recreation network of CZTHGH exhibits a multi-center, clustered spatial pattern, with a weakened radiative influence from core to peripheral areas. While recreational behaviors are increasingly fragmented and localized, this has not undermined the network’s overall function; instead, it has fostered systemic adaptability through multiple, functionally complementary clusters, accompanied by a marked shift in activity preference toward ecologically oriented spaces such as arbor forests, shrublands, and scenic forests, alongside a significant decline in non-forest recreation. Furthermore, a high degree of spatial alignment is observed among recreation supply nodes, public demand, and forest resources, indicating synergistic spatial coordination between recreational use and ecological conservation. Findings support an analytical framework integrating recreation supply, recreation demand, and forest resources, providing practical references for the sustainable use of ecological spaces in similar urban areas. Full article

The rise of digital platforms has transformed heritage interpretation from a single official narrative to multi-stakeholder participation. This study investigates how such platforms mediate the formation of a sense of place at the Kulangsu World Heritage Site (WHS). Data were collected from official narrative texts and user-generated content (UGC) on Dianping and Ctrip, and analyzed using high-frequency word statistics and semantic network analysis. The results reveal a clear divergence between official narratives, which emphasize Outstanding Universal Value (OUV), and tourist perceptions, which focus on visual landmarks and “check-in” practices shaped by the “digital gaze.” Moreover, the sense of place is shown to be a dynamic process, co-constructed through pre-visit expectations, on-site experiences, and post-visit reflections. The findings also highlight a transformation in tourists’ roles, shifting from passive cultural consumers to active participants in the co-construction of heritage values, with digital platforms serving as critical mediators. Theoretically, the study advances digital heritage scholarship by clarifying the mechanism of the digital gaze and the dynamic nature of sense of place. Practically, it underscores the importance of integrating official narratives with UGC to strengthen OUV communication, foster broader public engagement, and support the sustainable development of WHSs. Full article

Against the backdrop of the rapid proliferation of electric vehicles (EVs), gender-oriented behavioral mechanisms remain underexplored, particularly the unique pathways of female users in usage experience, value assessment, and purchase decision-making. This study constructs an integrated framework based on the Stimulus–Organism–Response (SOR) model, leveraging social media big data to analyze in depth how gender differences influence EV users’ purchase intentions. By integrating natural language processing techniques, grounded theory coding, and structural equation modeling (SEM), this study models and analyzes 272,083 pieces of user-generated content (UGC) from Chinese social media platforms, identifying key functional and emotional factors shaping female users’ perceptions and attitudes. The results reveal that esthetic value, safety, and intelligent features more strongly drive emotional responses among female users’ decisions through functional cognition, with gender significantly moderating the pathways from perceived attributes to emotional resonance and cognitive evaluation. This study further confirms the dual mediating roles of functional cognition and emotional experience and identifies a masking (suppression) effect for the ‘intelligent perception’ variable. Methodologically, it develops a novel hybrid paradigm that integrates data-driven semantic mining with psychological behavioral modeling, enhancing the ecological validity of consumer behavior research. Practically, the findings provide empirical support for gender-sensitive EV product design, personalized marketing strategies, and community-based service innovations, while also discussing research limitations and proposing future directions for cross-cultural validation and multimodal analysis. Full article

Digital transformation has re-engineered tourism marketing and how destination branding competes for tourist attention, yet scholarship offers little systematic quantification of these changes. Drawing on 160 peer-reviewed studies published between 1990 and 2025, we combine grounded-theory thematic synthesis with a random-effect meta-analysis of 60 datasets to trace branding performance across five technological eras (pre-Internet and brochure era: to mid-1990s; Web 1.0: 1995–2004; Web 2.0: 2004–2013; mobile first: 2013–2020; AI-XR: 2020–2025). Results reveal three structural shifts: (i) dialogic engagement replaces one-way promotion, (ii) credibility migrates to user-generated content, and (iii) artificial intelligence–driven personalisation reconfigures relevance, while mobile and virtual reality marketing extend immersion. Meta-analytic estimates show the strongest gains for engagement intentions (g = 0.57), followed by brand awareness (g = 0.46) and image (g = 0.41). Other equity dimensions (attitudes, loyalty, perceived quality) also improved on average, but to a lesser degree. Visual, UGC-rich, and influencer posts on highly interactive platforms consistently outperform brochure-style content, while robustness checks (fail-safe N, funnel symmetry, leave-one-out) confirm stability. We conclude that digital tools amplify, rather than replace, co-creation, credibility, and context. By fusing historical narrative with statistical certainty, the study delivers a data-anchored roadmap for destination marketers, researchers, and policymakers preparing for the AI-mediated decade ahead. Full article

With the rapid expansion of user-generated content (UGC) platforms, digital empowerment has emerged as a crucial strategy for enhancing platform competitiveness. This study investigates how different digital empowerment models—no digital empowerment, free digital empowerment, and paid digital empowerment—affect platform outcomes. Employing a Salop model with multi-homing content creators, we analyze the impact of empowerment strategies on content quality, consumer engagement, and platform profitability. The findings reveal that digital empowerment functions not only as a technical tool but also as an incentive mechanism and structural supply adjustment strategy. It creates a behavioral feedback loop whereby empowered creators improve content quality, which increases consumer retention and word-of-mouth dissemination, thereby boosting traffic, advertising revenue, and overall platform profit. Among the three models, paid digital empowerment generally yields the highest content quality and platform profitability. The effectiveness of each strategy is moderated by creators’ data utilization capability, market competition intensity, advertising monetization potential, and word-of-mouth effects. These results provide theoretical and managerial insights into the design of differentiated empowerment strategies that align with platform goals and creator characteristics in competitive UGC environments. Full article

The East–West Mountain Region (EWMR) of Wuhan is a vital natural and cultdural asset, characterized by its scenic nature landscapes and rich historical and cultural heritage. This study aims to address the problems of landscape character degradation and weakened public preferences caused by rapid urbanization and proposes a research framework integrating landscape character assessment and public preferences. Initially, we utilize K-means cluster analysis to identify landscape character types based on six landscape elements, resulting in a landscape character map with 20 types. Subsequently, we employ emotion analysis based on Natural Language Processing (NLP) techniques to analyze user-generated content (UGC) from Weibo check-in data to establish perception characteristic indicators reflecting public preferences. Finally, we quantitatively identify the environmental factors influencing public preferences through the SoIVES model and compare and integrate the landscape character map with the public emotion value map. The results show that (1) public preferences hotspots are concentrated in three types: (a) urban construction-driven types, including areas dominated by commercial service functions and those characterized by mixed-function residential areas; (b) natural terrain-dominated types with well-developed supporting facilities; and (c) hybrid transition types predominated by educational and scientific research land uses. These areas generally feature a high degree of functional diversity and good transportation accessibility. (2) Landscapes eliciting stronger emotional responses integrate moderate slopes, multifunctional spaces, and robust public services, whereas areas with weaker responses are characterized by single-function use or excessive urbanization. (3) The emotional variations within categories could be influenced by (a) functional hybridity through enhanced environmental exploration; (b) spatial usage frequency through place attachment formation; and (c) visual harmony through cognitive overload prevention. These findings provide critical insights for formulating zoning optimization plans aimed at the refined conservation and utilization of urban landscape resources, as well as offering guidance for improving landscape planning and management in the EWMR. Full article

Algorithmic recommendation mechanisms of social media platforms, viral diffusion of user-generated content (UGC), and real-time public opinion pressures are fundamentally deconstructing the traditional corporate crisis response paradigm that used to rely on one-way statements and delayed reactions. This compels enterprises to elevate their crisis response standards and construct new response frameworks. Based on an empirical analysis of 3,135,675 social media dissemination data points from 94 corporate crisis incidents, this study explores effective crisis response patterns for enterprises through three dimensions: response timing, methods, and content. The key findings indicate that traditional crisis response timelines prove inadequate for social media scenarios, whereas intervention during the ascending phase of dissemination significantly curtails crisis propagation cycles. Beyond formal statements, informal responses demonstrate equivalent mitigation effects, with combined formal–informal approaches yielding optimal outcomes. The comparative analysis of four content strategies (downplaying, supporting, denying, and reframing) reveals differentiated impacts on dissemination volume and duration, highlighting an inherent trade-off between these parameters. This research contributes to the crisis management theory in social media contexts while providing actionable guidance for enterprises to establish systematic crisis response methodologies. The results emphasize temporal sensitivity in response deployment, strategic content formulation, and multimodal communication integration. Full article

Cultural ecosystem services (CES) provided by 12 major urban parks on the Macau Peninsula were quantitatively evaluated using social media data. Furthermore, the potential implications of these findings for cultural heritage preservation and landscape design optimization were investigated. A CES evaluation framework consisting of six dimensions was established. User-generated content (UGC) was then collected using web-crawling techniques. A sentiment analysis based on natural language processing (NLP) and a spatial clustering analysis were subsequently performed on the collected data. Significant differences between CES dimensions were identified across Macau’s urban parks (F = 19.45, p < 0.01). Among the CES dimensions, esthetic appreciation (M = 76.04) and landscape experience (M = 72.47) received the highest scores. Moreover, landscape experience was found to have the strongest influence on visitors’ emotional evaluations (β = 0.95, p < 0.01). The spatial autocorrelation analysis indicated that the CES distribution did not exhibit significant clustering patterns (Z = 0.59). Based on these findings, it is recommended that educational functions be enhanced, public facilities upgraded, and local cultural heritage integrated into landscape and spatial planning. This research provides a novel CES quantification method based on social media data. It also offers theoretical and practical frameworks for urban planning and cultural heritage conservation in Macau. Full article

As the automotive industry advances rapidly, user needs are in a constant state of evolution. Driven by advancements in big data, artificial intelligence, and natural language processing, mining user requirements from user-generated content (UGC) on social media has become an effective way to understand these dynamic needs. While existing technologies have progressed in topic identification and sentiment analysis, single-method approaches often face limitations. This study proposes a novel method for user requirement mining based on BERTopic and RoBERTa, combining the strengths of topic modeling and sentiment analysis to provide a more comprehensive analysis of user needs. To validate this approach, UGC data from four major Chinese media platforms were collected. BERTopic was applied for topic extraction and RoBERTa for sentiment analysis, facilitating a linked analysis of user emotions and identified topics. The findings categorize user requirements into four main areas—performance, comfort and experience, price sensitivity, and safety—while also reflecting the increasing relevance of advanced features, such as sensors, powertrain performance, and other technologies. This method enhances user requirement identification by integrating sentiment analysis with topic modeling, offering actionable insights for automotive manufacturers in product optimization and marketing strategies and presenting a scalable approach adaptable across various industries. Full article