Purpose: Despite a significant focus on customer evaluation and sentiment analysis, limited attention has been paid to discrete emotional perspective in terms of the emotionality used in text. This paper aims to extend the general-sentiment dictionary in Chinese to a restaurant-domain-specific dictionary, visualize spatiotemporal sentiment trends, identify the main discrete emotions that affect customers’ ratings in a restaurant setting and identify constituents of influential emotions. Design/methodology/approach: A total of 683,610 online restaurant reviews downloaded from Dianping.com were analyzed by a sentiment dictionary optimized by the authors; the main emotions (joy, love, trust, anger, sadness and surprise) that affect online ratings were explored by using multiple linear regression methods. After tracking these sentiment review texts, Latent Dirichlet Allocation (LDA) and LDA models with term frequency-inverse document frequency as weights were used to find the factors that constitute influential emotions. Findings: The results show that it is viable to optimize or expand sentiment dictionary by word similarity. The findings highlight that love and anger have the highest effect on online ratings. The main factors that constitute consumers’ anger (local characteristics, incorrect food portions and unobtrusive location) and love (comfortable dining atmosphere, obvious local characteristics and complete supporting services) are identified. Different from previous studies, negativity bias is not observed, which poses a question of whether it has to do with Chinese culture. Practical implications: These findings can help managers monitor the true quality of restaurant service in an area on time. Based on the results, restaurant operators can better decide which aspects they should pay more attention to; platforms can operate better and can have more manageable webpage settings; and consumers can easily capture the quality of restaurants to make better purchase decisions. Originality/value: This study builds upon the existing general sentiment dictionary in Chinese and, to the best of the authors’ knowledge, is the first to provide a restaurant-domain-specific sentiment dictionary and use it for analysis. It also reveals the constituents of two prominent emotions (love and anger) in the case of restaurant reviews.

Identifying the influence factors lie behind the livelihood choices of rural households are of crucial significance for improving the sustainable livelihoods of rural households in tourism regions. Five villages in Sa Pa District, Vietnam, were selected in this study, to conduct household surveys and interviews with 180 households. Based on this, a comprehensive approach, which includes multinomial/binary logistic regression, Ripley’s function, and geographical detector, is applied to understand the households’ capital endowment and factors lie behind their livelihood choices. Results show that for rural households, tourism livelihood yields the highest income, but the lack of diversity of livelihood activities may make tourism livelihood household more vulnerable to the external risk and shocks than balanced livelihood households. Different types of households are found to show clustering feature, with clustering degree ranking as: agricultural > balanced > tourism > labour. Households with more natural capital are less likely to choose livelihoods other than agriculture livelihood. And households with more financial capital are less likely to engage in agricultural livelihood. Both financial capital and social capital can facilitate engagement in balanced livelihood. And financial capital is key to tourism livelihood, and a barrier impeding agricultural households to participate in other livelihood activities.

Purpose: This paper aims to clarify the effects of motivations on negative online customer reviews (OCRs) behavior in an integrative framework and to identify the moderating role of monetary compensation and psychological compensation in the Chinese food and beverage industry. Design/methodology/approach: Data were collected from 377 consumers who posted a negative review online. Hierarchical regression analyses were used to test the research hypotheses. Findings: The authors identified some characteristics of the consumers who posted negative online reviews in the Chinese food and beverage industry and found evidence that reveals the positive effects of emotional venting motivation and altruism motivation on posting negative customer online reviews. Economic motivation and self-enhancement motivation were not significantly connected to negative OCRs behaviors. Service recovery strategies can moderate the relationship between certain motivations and behaviors. The absence of psychological compensation will aggravate the influence of emotion venting motivation on consumers’ negative online reviews, while monetary compensation can restrain the influence of altruism motivation on negative online rating behavior. Research limitations/implications: This paper did not explore the effect of the fairness and timeliness of service recovery on negative OCRs behavior. This paper did not consider the different restaurant types and customers' characteristics, and future research can test similar models with different and more diverse samples. Practical implications: When implementing service recovery strategies, it is important to consider the psychological component of recovery. The absence of psychological compensation aggravates the influence of high levels of emotion venting motivation on consumers’ negative OCRs, leading to a lower rating, more word comments and negative photos. High levels of monetary compensation can restrain the influence of altruism motivation on negative online rating behavior. Originality/value: The current paper contributes to the hospitality management literature by investigating the motivations behind consumer decisions to post negative OCRs in a food and beverage context. In addition, the moderating effect that service recovery strategies have on this relationship was also explored in depth.