Search Results (74)

With the rapid advancement of information technology and the increasing maturity of online shopping platforms, cross-border shopping has experienced rapid growth. Online consumer reviews, as an essential part of the online shopping process, have become a vital way for merchants to obtain user feedback and gain insights into market demands. The research employs Python tools (Jupyter Notebook 7.0.8) to analyze the 14,078 pieces of review text data from the top four best-selling products in a certain product category on a certain cross-border e-commerce platform. By applying social network analysis, constructing LDA (Latent Dirichlet Allocation) topic models, and establishing LSTM (Long Short-Term Memory) sentiment classification models, the topics and sentiment distribution of the review set are obtained, and the evolution trends of topics and sentiments are analyzed according to different periods. The research finds that in the overall review set, consumers’ focus is concentrated on five aspects: functional features, quality and cost-effectiveness, usage effectiveness, post-purchase support, and design and assembly. In terms of changes in review sentiments, the negative proportion of the topics of functional features and usage effects is still relatively high. Given the above, this study integrates the 4P and 4C theories to propose strategies for enhancing the marketing capabilities of cross-border e-commerce in the context of digital cross-border operations, providing theoretical and practical marketing insights for cross-border e-commerce enterprises. Full article

Consumer satisfaction critically determines the operational sustainability of fresh food e-commerce platforms, yet integrated investigations combining multi-source data remain scarce. This study develops a theory–data fusion framework to identify key satisfaction drivers in China’s fresh e-commerce sector. Utilizing Python-based crawlers, we extracted 1252 online reviews of Aksu apples from a certain fresh produce e-commerce platform alongside 509 validated questionnaires. Through systematic literature synthesis, three core dimensions—perceived value (price–performance balance), platform experience (interface usability), and perceived quality (freshness assurance)—were operationalized into measurable indicators. The final structural equation model reveals that perceived value, platform experience, and perceived quality all have significant positive impacts on consumer satisfaction. This study pioneers a methodological paradigm integrating computational text mining (Octopus Collector + SPSS Pro) with traditional psychometric scales, achieving superior model fit (RMSEA = 0.023, CFI = 0.981). These findings empower platforms to implement a precision strategy. The validated framework provides a theoretical basis for omnichannel consumer research while addressing the data-source bias prevalent in prior studies. Full article

Online recommendation agents have demonstrated their value in various contexts by helping users navigate information overload, supporting decision-making, and influencing user behavior. There is a lack of studies focusing on recommendation systems for green hotels that utilize user-generated content from social networking and e-commerce platforms. While numerous studies have explored the use of real-world datasets for hotel recommendations, the development of recommendation systems specifically for green hotels remains underexplored, particularly in the context of Saudi Arabia. This study attempts to develop a new approach for green hotel recommendations using text mining and Long Short-Term Memory techniques. Latent Dirichlet Allocation is used to identify the main aspects of users’ preferences from the user-generated content, which will help the recommender system to provide more accurate recommendations to the users. Long Short-Term Memory is used for preference prediction based on numerical ratings. To better perform recommendations, a clustering technique is used to overcome the scalability issue of the proposed recommender system, specifically when there is a large amount of data in the datasets. Specifically, a spectral clustering algorithm is used to cluster the users’ ratings on green hotels. To evaluate the proposed recommendation method, 4684 reviews were collected from Saudi Arabia’s green hotels on the TripAdvisor platform. The method was evaluated for its effectiveness in solving sparsity issues, recommendation accuracy, and scalability. It was found that Long Short-Term Memory better predicts the customers’ overall ratings on green hotels. The comparison results demonstrated that the proposed method provides the highest precision (Precision at Top @5 = 89.44, Precision at Top @7 = 88.21) and lowest prediction error (Mean Absolute Error = 0.84) in hotel recommendations. The author discusses the results and presents the research implications based on the findings of the proposed method. Full article

Suzhou classical gardens, as a unique form of urban green space in China, not only embody rich historical and cultural heritage but also showcase distinctive natural landscapes, exerting a profound impact on modern mental health. This study employs text mining and content analysis methods to qualitatively explore online comments about Suzhou Gardens, which were collected using the Octopus Collector program to mine public reviews from the travel review platform Ctrip. These online reviews were further combined with questionnaire survey data to quantitatively analyze public preferences among different gardens and their restorative experience characteristics. We utilized the ROST CM6 software for high-frequency word extraction, semantic network analysis, and sentiment analysis to reveal the emotional perceptions of the public towards these gardens. The sentiment analysis results indicate that a majority of online comments express positive emotions, frequently mentioning words such as “tranquil”, “quiet”, and “serene”, highlighting the significant psychological comfort these spaces provide. Additionally, through one-way ANOVA and Pearson correlation analysis, we found significant differences in emotional and cognitive dimensions among different gardens, which are closely related to specific spatial factors such as landscape element diversity, visual scale, and types of greenery. These findings suggest that the spatial characteristics of Suzhou Gardens play a crucial role in shaping visitors’ emotional responses. Based on these insights, we proposed a series of design recommendations aimed at enhancing the overall image and healing functions of Suzhou Gardens. The findings of this study not only enrich the theoretical framework of healing landscape design but also provide valuable insights for the practical application of these principles in modern urban green space design. The research underscores the importance of combining functionality with aesthetic elements to meet the psychological needs of contemporary society. Full article

Transformative tourism experiences have attracted considerable scholarly interest in the recent past and deserve further attention to advance knowledge on the role of tourism in human transformation. This study aims to advance the understanding of the triggers and dimensions of transformative ecotourism experiences through the analysis of travelers’ online reviews relating to 10 Italian Pathways (Cammini d’Italia). A total of 742 reviews from 2010 to 2022 were collected from TripAdvisor using a web scraping procedure and analyzed by applying text mining techniques. This analysis explored the cognitive, affective, sensory, social and other experiential factors that can trigger tourists’ transformative experiences and their relationship with behavioral, psychological, spiritual and physical dimensions of transformation. The findings provide evidence of the association of cognitive and sensory triggers and the search for unusual, special tourism experiences to transformative experiences, with specific regard to the psychological, spiritual and physical dimensions. Based on the findings, theoretical and managerial implications are provided to improve the understanding and promotion of transformative tourism experiences in this context. Full article

This study examines the role of Komodo Island in boosting Indonesia’s status as a leading global tourism destination, emphasizing the importance of balancing environmental preservation with visitor satisfaction for sustainable growth. By conducting a comprehensive analysis of online reviews from Google Travel, this study identifies key factors that shape tourists’ experiences on Komodo Island. Specifically, the objectives are to uncover the primary drivers of visitor satisfaction and offer practical recommendations for tourism operators and policymakers. Using text mining and semantic network analysis through RStudio and UCINET 6.0 to analyze word associations, alongside exploratory factor analysis and linear regression in SPSS 29, this study focuses on aspects such as “Value for Money” and “Service Quality”. The results show that natural attractions greatly enhance visitor satisfaction, whereas high expenses and inconsistent service quality are sources of dissatisfaction. These insights highlight the importance of revisiting pricing approaches and enhancing training for frontline staff. The study’s recommendations for sustainable tourism on Komodo Island center on recalibrating pricing and improving service quality, fostering a memorable experience for visitors. Full article

Within the evolving field of sentiment analysis, the integration of topic modeling and association rule mining presents a promising yet underexplored method. This approach currently lacks an organized framework for maximizing insights that aid in drawing robust conclusions concerning customer sentiments. Therefore, this study addresses the need and rationale for having comprehensive sentiment analysis systems by integrating topic modeling and association rule mining to analyze online customer reviews of earphones sold on Amazon. It employs Bidirectional Encoder Representations from Transformers for Topic Modeling (BERTopic), a technique that generates coherent topics by effectively capturing contextual information, and Frequent Pattern Growth (FPGrowth), an efficient association rule mining algorithm used for discovering patterns and relationships in a dataset without candidate generation. This analysis of reviews on ten earphone products identified key customer concerns as follows: sound quality, noise cancellation, durability, and battery life. The results indicate an overall positive sentiment towards sound quality and battery life, mixed reviews on noise cancellation, and significant dissatisfaction with product durability. Using integrated topic modeling and association rule mining offers deeper insights into customer preferences and highlights specific areas for product improvement and guiding targeted marketing strategies. Moreover, we focused on algorithm selection to improve the model’s performance and efficiency, ensuring effective compatibility with our sentiment analysis framework. This study demonstrates how combining advanced data mining techniques and structuring insights from written customer feedback enhances the depth and clarity of sentiment analysis, furthering its applicability in e-commerce research. Full article

Variations in the genetic code for proteins are considered to confer traits and underlying disease. Identifying the functional consequences of these genetic variants is a challenging endeavor. There are online databases that contain variant information. Many publications also have described variants in detail. Furthermore, there are tools that allow for the prediction of the pathogenicity of variants. However, navigating these disparate sources is time-consuming and sometimes complex. Finally, text mining and large language models offer promising approaches to understanding the textual form of this knowledge. This review discusses these challenges and the online resources and tools available to facilitate this process. Furthermore, a computational framework is suggested to accelerate and facilitate the process of identifying the phenotype caused by a particular genetic variant. This framework demonstrates a way to gather and understand the knowledge about variants more efficiently and effectively. Full article

We aimed to provide a realistic portrayal of customer sentiment and product competitiveness, as well as to inspire businesses to optimise their products and enhance their services. This paper uses 119,190 pairs of real composite review data as a corpus to examine customer sentiment analysis and product competitiveness. The research is conducted by combining TF-IDF text mining with a time-phase division through the k-means clustering method. The study identified ‘quality’, ‘taste’, ‘appearance packaging’, ‘logistics’, ‘prices’, ‘service’, ‘evaluations’, and ‘customer loyalty’ as the commodity dimensions that customers are most concerned about. These dimensions should therefore serve as the primary entry point for improving the commodity and understanding customers. A review of customer feedback reveals that the composite reviews can be divided into three time stages. Furthermore, the sentiment expressed by customers can become increasingly negative over time. The product competitiveness based on the composite review can be characterised by four regional quadrants, such as ‘Advantage Area’, ‘Struggle Area’, ‘Opportunity Area’, and ‘Waiting Area’, and merchants can target these areas to improve product competitiveness according to the dimensional distribution. In the future, it will also be possible to take customer demographics into account in order to gain a deeper understanding of the customer base. Full article

This study explores the changing dynamics of tourism in Antarctica, focusing on the impact of digitalisation and User-Generated Content on platforms like Tripadvisor. It aims to understand how online reviews influence perceptions and decisions to visit Antarctica, a region known for its pristine environment and status as ‘the last frontier’. Utilising Environmental Perception and Behaviour Geography (EPBG) principles, this research conducts a quantitative analysis of reviews from potential and current travellers. Through text mining, topic modelling, sentiment analysis, and Natural Language Processing (NLP), it investigates the emotional and perceptual discourse surrounding Antarctic tourism and its alignment with Agenda 2030 and Sustainable Development Goals. The findings reveal a detailed narrative of sustainability challenges and the emotional geography related to tourism in Antarctica, highlighting emotions such as happiness, anger, surprise, fear, disgust, and sadness among visitors. This study uncovers differences in perception based on visitors’ backgrounds, noting that individuals from nature-focused cities display strong environmental concerns, whereas those from advanced urban centres show a more positive attitude. This research contributes to the understanding of EPBG, text mining, and NLP, offering insights into sustainable tourism practices in Antarctica. Full article

The incessant evolution of online platforms has ushered in a multitude of shopping modalities. Within the food industry, however, assessing the delectability of meals can only be tentatively determined based on consumer feedback encompassing aspects such as taste, pricing, packaging, service quality, delivery timeliness, hygiene standards, and environmental considerations. Traditional text data mining techniques primarily focus on consumers’ emotional traits, disregarding pertinent information pertaining to the online products themselves. In light of these aforementioned issues in current research methodologies, this paper introduces the Bert BiGRU Softmax model combined with multimodal features to enhance the efficacy of sentiment classification in data analysis. Comparative experiments conducted using existing data demonstrate that the accuracy rate of the model employed in this study reaches 90.9%. In comparison to single models or combinations of three models with the highest accuracy rate of 7.7%, the proposed model exhibits superior accuracy and proves to be highly applicable to online reviews. Full article

As the leading platform of online education, MOOCs provide learners with rich course resources, but course designers are still faced with the challenge of how to accurately improve the quality of courses. Current research mainly focuses on learners’ emotional feedback on different course attributes, neglecting non-emotional content as well as the costs required to improve these attributes. This limitation makes it difficult for course designers to fully grasp the real needs of learners and to accurately locate the key issues in the course. To overcome the above challenges, this study proposes an MOOC improvement method based on text mining and multi-attribute decision-making. Firstly, we utilize word vectors and clustering techniques to extract course attributes that learners focus on from their comments. Secondly, with the help of some deep learning methods based on BERT, we conduct a sentiment analysis on these comments to reveal learners’ emotional tendencies and non-emotional content towards course attributes. Finally, we adopt the multi-attribute decision-making method TOPSIS to comprehensively consider the emotional score, attention, non-emotional content, and improvement costs of the attributes, providing course designers with a priority ranking for attribute improvement. We applied this method to two typical MOOC programming courses—C language and Java language. The experimental findings demonstrate that our approach effectively identifies course attributes from reviews, assesses learners’ satisfaction, attention, and cost of improvement, and ultimately generates a prioritized list of course attributes for improvement. This study provides a new approach for improving the quality of online courses and contributes to the sustainable development of online course quality. Full article

Observing customers is one of the methods to uncover their needs. By closely observing how customers use products, we can indirectly experience their interactions and gain a deep understanding of their feelings and preferences. Through this process, companies can design new products that have the potential to succeed on the market. However, traditional methods of customer observation are time-consuming and labor-intensive. In this study, we propose a method that leverages the analysis of online customer reviews as a substitute for direct customer observations. By correlating a customer journey map (CJM) with online reviews, this research establishes a verb-centric analysis that produces a CJM based on online review data. Various text analysis techniques were utilized in this process. When applying online retail site review data, our method of customer observation required one week. This proved to be more efficient in comparison with traditional customer observation methods, which typically need at least one month to complete. Additionally, we observed that the customer behavior-based VOC (voice of customer) identified during the CJM mapping process offers broad insights that are distinct from traditional product feature-centric review analyses. This behavior VOC can be effectively utilized for product improvement, new product development, and product marketing. To verify the usefulness of the behavior VOC, we asked product development experts to evaluate the quantitative analysis results of the same reviews. The experts evaluated the CJM as useful for product conceptualization and selecting technology priorities. Full article

Meeting the personalized needs of users is the key to achieving the sustainable success of a product. It depends not only on the product’s functionality but also on satisfying users’ emotional needs for the product’s appearance. Therefore, researchers have been conducting research focusing on Kansei engineering theory to determine users’ emotional needs effectively. The initial process involves accurately extracting and filtering emotional data and Kansei words from consumers. Thus, we propose an evaluation model to efficiently obtain, screen, and sort these Kansei words based on Kansei engineering, using household portable air conditioners as research subjects. By integrating techniques for online user comment mining methods, users’ Kansei terms related to the product’s appearance can be gathered efficiently. These terms are then combined with image samples and filtered to determine a final set of 16 Kansei word pairs. Subsequently, the fuzzy analytic hierarchy process (FAHP) is utilized to prioritize these terms, and the fuzzy comprehensive evaluation (FCE) method is used to validate the results and determine the applicability of the evaluation model. The results showed that Kansei words could be quickly and objectively acquired using existing text mining techniques on online reviews. Moreover, the weights of different Kansei terms of the product’s appearance in the consumer’s perception are accurately produced through the FAHP. This evaluation model marks a significant advancement in accurately obtaining users’ emotional data in Kansei engineering. It offers valuable guidance for designing products that meet users’ personalized needs, enhancing design efficiency and reducing resource wastage at the early stages of designing, and improving the sustainability development of Kansei engineering. Full article

The tourism sector plays a crucial role in the global economy, encompassing both physical infrastructure and cultural engagement. Indonesia has a wide range of attractions and has experienced remarkable growth, with Bali as a notable example of this. With the rapid advancements in technology, travelers now have the freedom to explore independently, while online travel agencies (OTAs) serve as important resources. Reviews from tourists significantly impact the service quality and perception of destinations, and text mining is a valuable tool for extracting insights from unstructured review data. This research integrates multiclass text classification and a network analysis to uncover tourists’ behavioral patterns through their perceptions and movement. This study innovates beyond conventional sentiment and cognitive image analysis to the tourists’ perceptions of cognitive dimensions and explores the sentiment correlation between different cognitive dimensions. We find that destinations generally receive positive feedback, with 80.36% positive reviews, with natural attractions being the most positive aspect while infrastructure is the least positive aspect. We highlight that qualitative experiences do not always align with quantitative cost-effectiveness evaluations. Through a network analysis, we identify patterns in tourist mobility, highlighting three clusters of attractions that cater to diverse preferences. This research underscores the need for tourism destinations to strategically adapt to tourists’ varied expectations, enhancing their appeal and aligning their services with preferences to elevate destination competitiveness and increase tourist satisfaction. Full article