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This study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, thus advancing the literature and providing practical insights into electronic word-of-mouth management for the industry.

This study elaborates a hybrid model that integrates deep learning (DL) and a sentiment lexicon (SL) and compares it to five other models, including SL, random forest (RF), naïve Bayes, support vector machine (SVM) and a DL model, for the task of emotion recognition in restaurant online reviews. These models are trained and tested using 652,348 online reviews from 548 restaurants.

The hybrid approach performs well for valence-based emotion and discrete emotion recognition and is highly applicable for mining online reviews in a restaurant setting. The performances of SL and RF are inferior when it comes to recognizing discrete emotions. The DL method and SVM can perform satisfactorily in the valence-based emotion recognition.

These findings provide methodological and theoretical implications; thus, they advance the current state of knowledge on emotion recognition in restaurant online reviews. The results also provide practical insights into intelligent service quality monitoring and electronic word-of-mouth management for the industry.

This study proposes a superior model for emotion recognition in restaurant online reviews. The methodological framework and steps are elucidated in detail for future research and practical application. This study also details the performances of other commonly used models to support the selection of methods in research and practical applications.

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.

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.

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.

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.

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.

This study aims to investigate how small talk interaction affects knowledge sharing, examining the mediating role of interpersonal trust (affect- and cognition-based trust) and the moderating role of perceived similarity among the mechanisms of small talk and knowledge sharing.

This research conducts complementary studies and collects multi-culture and multi-wave data to test research hypotheses and adopts structural equation modeling to validate the whole conceptual model.

The research findings first reveal two trust mechanisms linking small talk and knowledge sharing. Meanwhile, the perceived similarity between employees, specifically, strengthens the affective pathway of trust rather than the cognitive pathway of trust.

This study combines Interaction Ritual Theory and constructs a dual-facilitating pathway approach that aims to reveal the impact of small talk on knowledge sharing, describing how and when small talk could generate a positive effect on knowledge sharing. This research provides intriguing and dynamic insights into understanding knowledge sharing processes.

The aim of this study is to examine the relationships between taking charge, bootlegging innovation and innovative job performance, and to explore the moderating roles of felt responsibility for constructive change (FRCC) and creative self-efficacy (CSE).

Data for this research was collected from 503 employees working in a chain company. Through a longitudinal study design, a three-wave survey with 397 valid data provided support for the proposed theoretical model.

The results maintain a positive association between taking charge, bootlegging innovation and innovative job performance, indicating the mediating effect of bootlegging innovation. Additionally, both the FRCC and CSE facilitate the indirect effect of taking charge on innovative job performance through bootlegging innovation. Furthermore, the integrated moderated mediation model analysis suggested that FRCC is more vital in improving employees' innovative job performance.

This research aims to break the black box between taking charge and innovative job performance, which has been relatively unexplored. Drawing from self-determination theory (SDT) and the proactive motivation model, the authors verify the bridge-building role of bootlegging innovation and the dual-facilitating effects of FRCC and CSE while employees conduct taking charge. This study’s results provide new insight for managers to foster, encourage and support employees' proactive behavior.

The purpose of constructing the technology/function matrix is to analyze the patents in the target domain. The extraction of technology words is an important part of the construction of technology/function matrix. This algorithm is used to solve the problem of low efficiency of traditional Chinese process patents technology words extraction.

The authors propose a Chinese process patents technology words extraction method based on the improved term frequency–inverse document frequency (TF-IDF) algorithm to help technicians obtain the technology words in the target domain. According to the characteristics of Chinese process patents technology words, the TF value of candidate technology words is divided into four parts, and the corpus of IDF value calculation of candidate technology words is selected.

Through the test of Chinese process patents in the domain of path planning, this study shows that the method is feasible and practical. It can help users quickly and accurately obtain the technology words of Chinese process patents in the target domain.

With the increasing number of patents on the network-based patent information platform, patent analysis of massive Chinese process patents has become a research focus. The method proposed in this paper can facilitate users to extract technology words from massive Chinese process patents for patent analysis.

This paper aims to improve the efficiency of Chinese process patents technology words extraction. The authors hope that the proposed method can reduce the labor and time cost of Chinese process patents technology words extraction.