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The purpose of this paper is to expand and analyze deeply customer emotions, concretize the levels of positive or negative emotions with the aim of using machine learning methods, and build a model to identify customer emotions.

The study proposed a customer emotion detection model and data mining method based on the collected dataset, including 80,593 online reviews on agoda.com and booking.com from 2009 to 2022.

By discerning specific emotions expressed in customers' comments, emotion detection, which refers to the process of identifying users' emotional states, assumes a crucial role in evaluating the brand value of a product. The research capitalizes on the vast and diverse data sources available on hotel booking websites, which, despite their richness, remain largely unexplored and unanalyzed. The outcomes of the model, pertaining to the detection and classification of customer emotions based on ratings and reviews into four distinct emotional states, offer a means to address the challenge of determining customer satisfaction regarding their actual service experiences. These findings hold substantial value for businesses operating in this domain, as the findings facilitate the evaluation and formulation of improvement strategies within their business models. The experimental study reveals that the proposed model attains an exact match ratio, precision, and recall rates of up to 81%, 90% and 90%, respectively.

The study has yet to mine real-time data. Prediction results may be influenced because the amount of data collected from the web is insufficient and preprocessing is not completely suppressed. Furthermore, the model in the study was not tested using all algorithms and multi-label classifiers. Future research should build databases to mine data in real-time and collect more data and enhance the current model.

The study's results suggest that the emotion detection models can be applied to the real world to quickly analyze customer feedback. The proposed models enable the identification of customers' emotions, the discovery of customer demand, the enhancement of service, and the general customer experience. The established models can be used by many service sectors to learn more about customer satisfaction with the offered goods and services from customer reviews.

The research paper helps businesses in the hospitality area analyze customer emotions in each specific aspect to ensure customer satisfaction. In addition, managers can come up with appropriate strategies to bring better products and services to society and people. Subsequently, fostering the growth of the hotel tourism sector within the nation, thereby facilitating sustainable economic development on a national scale.

This study developed a customer emotions detection model for detecting and classifying customer ratings and reviews as 4 specific emotions: happy, angry, depressed and hopeful based on online booking hotel websites agoda.com and booking.com that contains 80,593 reviews in Vietnamese. The research results help businesses check and evaluate the quality of their services, thereby offering appropriate improvement strategies to increase customers' satisfaction and demand more effectively.

This study aims to analyse online customer experience in the hospitality industry through dynamic topic modelling (DTM) and net promoter score (NPS). A novel model that was used for collecting, pre-processing and analysing online reviews was proposed to understand the hidden information in the corpus and gain customer experience.

A corpus with 259,470 customer comments in English was collected. The researchers experimented and selected the best K parameter (number of topics) by perplexity and coherence score measurements as the input parameter for the model. Finally, the team experimented on the corpus using the Latent Dirichlet allocation (LDA) model and DTM with K coefficient to explore latent topics and trends of topics in the corpus over time.

The results of the topic model show hidden topics with the top high-probability keywords that are concerned with customers and the trends of topics over time. In addition, this study also calculated and analysed the NPS from customer rating scores and presented it on an overview dashboard.

The data used in the experiment are only a part of all user comments; therefore, it may not reflect all of the current customer experience.

The management and business development of companies in the hotel industry can also benefit from the empirical findings from the topic model and NPS analytics, which will support decision-making to help businesses improve products and services, increase existing customer satisfaction and draw in new customers.

This study differs from previous works in that it attempts to fill a gap in research focused on online customer experience in the hospitality industry and uses text analytics and NPS to reach this goal.

本研究旨在通过动态主题建模和净推荐值分析酒店业的在线客户体验。 提出了一种用于收集、预处理和分析在线评论的新模型, 以了解语料库中的隐藏信息并获得客户体验。

收集了一个包含 259,470 条英文客户评论的语料库。 研究人员通过 Perplexity 和 Coherence Score 测量结果进行了实验, 并选择了最佳的 K 参数（主题数量）作为模型的输入参数。 最后, 团队使用 Latent Dirichlet allocation (LDA) 模型和具有 K 系数的 Dynamic Topic Model (DTM) 在语料库上进行实验, 以探索语料库中的潜在主题和主题随时间变化的趋势。

主题模型的结果显示了隐藏的主题, 其中包含与客户相关的顶级高概率关键字以及主题随时间的变化趋势。 此外, 该研究还根据客户评分计算和分析净推荐值 (NPS), 并将其显示在概览仪表板上。

实验中使用的数据只是所有用户评论的一部分; 因此, 它可能无法反映所有当前的客户体验。

酒店业公司的管理和业务发展也可以受益于主题模型和 NPS 分析的实证结果, 这将支持决策制定, 帮助企业改进产品和服务, 提高现有客户满意度, 并吸引新客户 .

本研究不同于以往的研究, 因为它试图填补以酒店业在线客户体验为重点的研究空白, 并使用文本分析和 NPS 来实现这一目标。

This study aims to use the voice of the customer (VoC) strategy to collect user-generated content (UGC) compare customer expectations with reality, make the necessary improvements for the business and create personalized strategies for each customer to maximize revenue, focus on hospitality industry in Vietnam market.

This study proposes a synthesis of techniques for a deep understanding of the VoC based on online reviews in the hospitality industry. First, 409,054 comments were collected from websites in the hospitality sector. Second, the data will be organized, stored, cleaned, analyzed and evaluated. Next, research using business intelligence (BI) solutions integrating three models, including net promoter score (NPS), graph model and latent Dirichlet allocation (LDA), based on natural language processing (NLP) technique, experiment on Vietnamese and English data to explore the multidimensional voice of customer’s row. Finally, a dashboard system will be implemented to visualize analysis results and recommendations on marketing strategies to improve product and service quality.

Experimental results allow analysts and managers to “listen to the customer’s voice” accurately and effectively, identify relationships between entities, topics of discussion in favor of positive and negative trends.

The novelty in this study is the integration of three models, including NPS, graph model and LDA. These models are combined based on the BI solution and NLP technique. The study also conducted experiments on both Vietnamese and English languages, which ensures more effective practical application.

The purpose of this paper is to focus on psychological capital (PsyCap) – one of the emerging topics of human resource management, by examining its antecedents and outcomes through the lens of social exchange theory.

A meta-analytic approach was applied to validate the proposed hypotheses. Altogether, 105 primary studies published between 2000 and 2018 were collected and used.

Results show that leadership styles (authentic leadership, ethical leadership, abusive leadership) and organizational support are antecedents and desirable work attitudes (job satisfaction, organizational commitment, organizational citizenship behavior) are consequences of PsyCap. Employee’s characteristics significantly moderated the relationship between PsyCap and work attitudes.

This was the first attempt to examine PsyCap in a theoretical framework with its antecedents and outcomes and furthermore, to apply a meta-analytic method. The moderating role of employee characteristics in the relationship between PsyCap and work attitudes is also explored.

The problem of image retrieval and image description exists in various fields. In this paper, a model of content-based image retrieval and image content extraction based on the KD-Tree structure was proposed.

A Random Forest structure was built to classify the objects on each image on the basis of the balanced multibranch KD-Tree structure. From that purpose, a KD-Tree structure was generated by the Random Forest to retrieve a set of similar images for an input image. A KD-Tree structure is applied to determine a relationship word at leaves to extract the relationship between objects on an input image. An input image content is described based on class names and relationships between objects.

A model of image retrieval and image content extraction was proposed based on the proposed theoretical basis; simultaneously, the experiment was built on multi-object image datasets including Microsoft COCO and Flickr with an average image retrieval precision of 0.9028 and 0.9163, respectively. The experimental results were compared with those of other works on the same image dataset to demonstrate the effectiveness of the proposed method.

A balanced multibranch KD-Tree structure was built to apply to relationship classification on the basis of the original KD-Tree structure. Then, KD-Tree Random Forest was built to improve the classifier performance and retrieve a set of similar images for an input image. Concurrently, the image content was described in the process of combining class names and relationships between objects.

The demand for clean vegetables has rapidly increased, many farmers gradually turn to vegetable cultivation to increase income; therefore, agricultural cooperative mobilized farmers group to facilitate them access to technical training and enhance compliance with the Vietnamese Good Agricultural Practices (VietGAP) standards. The purpose of this paper is to evaluate the impacts of the participation on farmer’s income as well as the major factors that affect the participation in cooperatives by the vegetable farmers in Vietnam.

The study used primary data collected from vegetable farmers in Vietnam. This study utilized propensity score matching to avoid initial selection bias. The differences between participants and nonparticipants will be adjusted by matching each membership individual to a nonmember based on similar observable characteristics by summarizing the conditional probability of a member given pretreatment characteristics.

This study demonstrates that participation in cooperatives is significantly affected by ethnicity, age of household head, labor involving vegetable production and extension service access. The results of this study also confirm that agricultural cooperatives have positive effects on member farmers to enhance income and that participants – on average – have a higher income than nonparticipants.

The method cannot rule out the possibility of selection bias due to unobserved differences between participants and even an appropriate comparison group.

This study contributes to an improved understanding about impacts of cooperatives on farmers’ income in developing and emerging economies. Moreover, research also upgrades knowledge regarding the effectiveness of agricultural cooperatives in Vietnam, as well as guides policymakers in supporting the cooperatives in expanding the market and other necessary changes.