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The development of social media has led to large numbers of internet users now producing massive amounts of user-generated content (UGC). UGC, which shows users’ opinions about events directly, is valuable for monitoring public opinion. Current researches have focused on analysing topic evolutions in UGC. However, few researches pay attention to emotion evolutions of sub-topics about popular events. Important details about users’ opinions might be missed, as users’ emotions are ignored. This paper aims to extract sub-topics about a popular event from UGC and investigate the emotion evolutions of each sub-topic.

This paper first collects UGC about a popular event as experimental data and conducts subjectivity classification on the data to get subjective corpus. Second, the subjective corpus is classified into different emotion categories using supervised emotion classification. Meanwhile, a topic model is used to extract sub-topics about the event from the subjective corpora. Finally, the authors use the results of emotion classification and sub-topic extraction to analyze emotion evolutions over time.

Experimental results show that specific primary emotions exist in each sub-topic and undergo evolutions differently. Moreover, the authors find that performance of emotion classifier is optimal with term frequency and relevance frequency as the feature-weighting method.

To the best of the authors’ knowledge, this is the first research to mine emotion evolutions of sub-topics about an event with UGC. It mines users’ opinions about sub-topics of event, which may offer more details that are useful for analysing users’ emotions in preparation for decision-making.

This study aims to conduct a systematic review and critically analyze the sentiment analysis literature in hospitality and tourism from methodological (data sets and analyzes) and thematic (topics, theories, key constructs and their relationships) perspectives.

Qualitative thematic review and quantitative systematic review were performed on 70 papers obtained from hospitality and tourism categories of two databases, namely, Web of Science and Scopus.

A total of 5 topics and 27 sub-topics were identified and the major theme is market intelligence. Sentiment variables were investigated not only as independent but also as dependent variables. The customer rating is the most investigated dependent variable, whereas moderators and mediators were rarely tested. Most reviewed studies did not use theory. The findings from the methodological review show that analysis of big data was rare. Moreover, testing the performance of sentiment analyzes was uncommon, and only one paper tested the performance of aspect/feature extraction.

This study extends prior review studies by providing a comprehensive view of how knowledge and methodologies of sentiment analysis have developed. The identified themes and key constructs serve as a solid base for future knowledge advancement. Future research directions on sentiment analysis are also provided.

To the best of the authors’ knowledge, this study is the first comprehensive methodological and thematic review of sentiment analysis in hospitality and tourism. Based on the identified findings, the authors propose several directions for future research.

Job market shifts, such as workforce mobility and aging societies, cause the exit of knowledgeable personnel from organizations. The ensuing knowledge loss (K-loss) has broad negative effects. This study analyzes the knowledge management literature on K-loss published from 2000 to 2021 and identifies fruitful directions for future research.

The authors conduct a systematic literature review of 74 peer-reviewed articles published between 2000 and 2021. These articles were retrieved from ProQuest Central, Science Direct, EBSCOhost and Emerald databases. The analysis utilizes Jesson et al.’s (2011) six principles: field mapping, comprehensive search, quality assessment, data extraction, synthesis and write-up.

Three sub-topics emerge from the systematic literature review: K-loss drivers, positive and negative impacts of K-loss and mitigation strategies. Over half of the literature addresses mitigation strategies and provides solutions for K-loss already in progress, rather than proposing preventive measures.

This study has limitations related to the time span covered. Moreover, it focuses on articles published in refereed journals. Therefore, important contributions from conference papers, books and professional reports were excluded.

This research comprehensively synthesizes the K-loss literature and proposes future avenues of research to address under-investigated areas and potentially lead to theoretical and empirical advancements in the field. This study also provides suggestions for improving managerial practices.

Knowledge organization (KO) has been advancing at a progressively rapid pace under the influence of information technology. This study aims to explore the topics, characteristics, and trends of KO research in the 21st century.

The full text of 4,360 KO-related articles published from 2000 to 2021 is collected. Through content analysis, this study identifies the topics, research methods, and application areas of each article, and the statistics are presented through a series of visualizations.

In total, 13 main topics, 105 sub-topics, 16 research methods, and 57 application areas are identified. Notably, classification has always been an important topic, while linked data, automated techniques, and ontology have become popular topics recently. Significant changing features have also occurred. The versatile use of research methods has increased, with empirical research becoming the mainstream. Application areas show a trend of refinement from subject areas to specific scenarios. Construction techniques present a combination of automated techniques, crowdsourcing, and experts.

KO has evolved and diversified due to technological developments. This study is the first to focus on the continuous changing features over an extended, 21-year period, as opposed to sampling a few years. It also provides clues and insights for researchers and practitioners interested in KO to understand how it has changed in the Semantic Web and big data context.

Systems to support literature review (LR) and manuscript preparation tend to focus on only one or two of the tasks involved. The purpose of this paper is to describe an intervention framework that redesigns a particular set of tasks, allowing for interconnectivity between the tasks and providing appropriate user interface display features for each task in a prototype system.

A user evaluation study was conducted on the prototype system. The system supports the three tasks: building a reading list (RL) of research papers, finding similar papers based on a set of papers and shortlisting papers from the final RL for inclusion in manuscript based on article type. A total of 119 researchers who had experience in authoring research papers, participated in the evaluation study. They had to select one of the provided 43 topics and execute the tasks offered by the system. Three questionnaires were provided for evaluating the tasks and system. Both quantitative and qualitative analyses were performed on the collected evaluation data.

Task redesign aspects had a positive impact in user evaluation for the second task of finding similar papers while improvement was found to be required for the first and third tasks. The tasks interconnectivity features seed basket and RL were helpful for the participants in conveniently searching for papers within the system. Two of the four proposed informational display features, namely, information cue labels and shared co-relations were the most preferred features of the system. Student user group found the task recommendations and the overall system to be more useful and effective than the staff group.

This study validates the importance of interconnected task design and novel informational display features in accentuating task-based recommendations for LR and manuscript preparatory tasks. The potential for improvement in recommendations was shown through the task redesign exercise where new requirements for the tasks were identified. The resultant prototype system helps in bridging the gap between novices and experts in terms of LR skills.

The purpose of this paper is to review the state of existing literature for various corporate governance models by answering specific questions. Much has been written in the recent years on various corporate governance (CG) models, primarily the model of Anglo-Saxon and Continental European. In particular, it investigates most examined model in literature, forums used to publish and research types conducted, as well as basic differences between the two models. Findings of this paper suggest that more evidence-based systematic reviews on various aspects and geographical regions are needed to map the entire field of CG.

Much has been written in the recent years on various CG models, primarily the model of Anglo-Saxon and Continental European. This paper attempts to review the state of existing literature for these models by answering specific questions. In particular, it investigates most examined model in literature, forums used to publish and research types conducted, as well as basic differences between the two models. Findings of this paper suggest that more evidence-based systematic reviews on various aspects and geographical regions are needed to map the entire field of CG.

The authors found that although both models are well-reviewed, Continental European model is mostly explored with 47 per cent contributions as compared to Anglo-Saxon with 45 per cent. Moreover, majority of contributions are based on analytical research in terms of research type (30 per cent) and primarily focus on convergence of models. In addition, some 85 per cent of selected studies are based on theoretical research work, which leads to a significant dearth of empirical studies in the literature.

The scope of the paper is limited to two basic models of CG, namely, Anglo-Saxon model and Continental European model in context of specific research questions.

The systematic review on the basic models will assist the practitioners and policy-makers in determining the status of existing literature based on evidences. Further, it may facilitate in formulating new laws, regulations, codes and policies.

The authors used evidenced-based systematic approach for conducting literature review of CG models. Systematic review is getting much attention of researchers, as it minimizes the bias by adopting a replicable, scientific and transparent process. This review, as contrary to narrative, contributes to the CG models literature the findings based on evidences.

With the rapid development of social media, the occurrence and evolution of emergency events are often accompanied by massive users' expressions. The fine-grained analysis on users' expressions can provide accurate and reliable information for event processing. Hence, 2,003,814 expressions on a major malignant emergency event were mined from multiple dimensions in this paper.

This paper conducted finer-grained analysis on users' online expressions in an emergency event. Specifically, the authors firstly selected a major emergency event as the research object and collected the event-related user expressions that lasted nearly two years to describe the dynamic evolution trend of the event. Then, users' expression preferences were identified by detecting anomic expressions, classifying sentiment tendencies and extracting topics in expressions. Finally, the authors measured the explicit and implicit impacts of different expression preferences and obtained relations between the differential expression preferences.

Experimental results showed that users have both short- and long-term attention to emergency events. Their enthusiasm for discussing the event will be quickly dispelled and easily aroused. Meanwhile, most users prefer to make rational and normative expressions of events, and the expression topics are diversified. In addition, compared with anomic negative expressions, anomic expressions in positive sentiments are more common. In conclusion, the integration of multi-dimensional analysis results of users' expression preferences (including discussion heat, preference impacts and preference relations) is an effective means to support emergency event processing.

To the best of the authors' knowledge, it is the first research to conduct in-depth and fine-grained analysis of user expression in emergencies, so as to get in-detail and multi-dimensional characteristics of users' online expressions for supporting event processing.

The authors introduce two semantics-based navigation applications that facilitate information-seeking activities in internal link-based web sites in Wikipedia. These applications aim to help users find concepts within a topic and related articles on a given topic quickly and then gain topical knowledge from internal link-based encyclopedia web sites. The paper aims to discuss these issues.

The WNavi s application consists of three information visualization (IV) tools which are a topic network, a hierarchy topic tree and summaries for topics. The WikiMap application consists of a topic network. The goal of the topic network and topic tree tools is to help users to find the major concepts of a topic and identify relationships between these major concepts easily. In addition, in order to locate specific information and enable users to explore and read topic-related articles quickly, the topic tree and summaries for topics tools support users to gain topical knowledge quickly. The authors then apply the k-clique of cohesive indicator to analyze the sub topics of the seed query and find out the best clustering results via the cosine measure. The authors utilize four metrics, which are correctness, time cost, usage behaviors, and satisfaction, to evaluate the three interfaces. These metrics measure both the outputs and outcomes of applications. As a baseline system for evaluation the authors used a traditional Wikipedia interface. For the evaluation, the authors used an experimental user study with 30 participants.

The results indicate that both WikiMap and WNavi s supported users to identify concepts and their relations better compared to the baseline. In topical tasks WNavi s over performed both WikiMap and the baseline system. Although there were no time differences in finding concepts or answering topical questions, the test systems provided users with a greater gain per time unit. The users of WNavi s leaned on the hierarchy tree instead of other tools, whereas WikiMap users used the topic map.

The findings have implications for the design of IR support tools in knowledge-intensive web sites that help users to explore topics and concepts.

The authors explored to what extent the use of each IV support tool contributed to successful exploration of topics in search tasks. The authors propose extended task-based evaluation measures to understand how each application provides useful context for users to accomplish the tasks and attain the search goals. That is, the authors not only evaluate the output of the search results, e.g. the number of relevant items retrieved, but also the outcome provided by the system for assisting users to attain the search goal.

This paper aims to develop and evaluate, in “real-life” pilot applications, a framework for advanced social media exploitation by government agencies in their policy-making processes to promote public participation and conduct crowdsourcing.

This framework has been developed through cooperation with public sector employees experienced in public policy-making, using both qualitative and quantitative techniques: semi-structured focus group discussions, scenarios development and questionnaire surveys. The evaluation of the framework has been conducted through semi-structured focus group discussions with public sector employees involved in the pilot applications.

A framework has been developed for advanced social media exploitation by government agencies, which is based on the automated posting of policy-related content to multiple social media, and then retrieval and processing of citizens’ interactions with it (e.g. views, likes, comments and retweets), using the application programming interfaces (API) of these social media. Furthermore, a supporting information and communication technologies (ICT) infrastructure and an application process model for it were developed. Its evaluation, based on “real-life” pilot applications, leads to useful insights concerning its capabilities, strengths and weaknesses.

The proposed framework has been evaluated in a small number of pilot applications, so further evaluation of it is required, in various types of government agencies and for different kinds of policy consultations.

The above framework enables government agencies to communicate with wider and more heterogeneous audiences in a short time and at a low cost, increase public participation in their policy-making processes, collect useful knowledge, ideas and opinions from citizens and, finally, design better, more socially rooted, balanced and realistic policies.

This research contributes to the development of knowledge concerning advanced practices for effective social media exploitation in government (which is currently limited, despite the considerable relevant knowledge developed in this area for the private sector), by developing and evaluating a framework for advanced and highly automated exploitation of multiple social media by government agencies. Furthermore, an evaluation methodology for such practices has been developed, which is based on sound theoretical foundations.

This research study aims to capture the movement of research focus and to highlight notable authors, journals and documents on teachers' satisfaction from 1956 to 2022.

This paper analyzed the bibliographic metadata associated with 720 Clarivate Web of Science indexed articles on this topic, using descriptive, co-occurrence and thematic analyses.

The study first identified the growth trajectory of literature on this topic over time and found a steady increasement since 2016. The findings also revealed four sub-topics that scholars often investigate when studying teachers' satisfaction, which are: leadership, engagement, self-efficacy and retention, as well as the scholars' research focus on those sub-topics across different periods. In recent years, scholars have been paying more attention to exploring the influence of various leadership styles and teachers' professional identities on job satisfaction.

This paper is the first bibliometric review on this topic. The results of this study showed that there was little collaboration between authors across countries and territories. Therefore, there is a considerable demand for comparative studies on this topic, which can expose untapped insights from various cultures and societies. Finally, by featuring the most influential authors, documents and journals on teachers' satisfaction, this work might serve as an introduction for scholars who are new to this topic.