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This study aims to explore the dissemination and evolutionary path of online public opinion from a crisis management perspective. By clarifying the influencing factors and dynamic mechanisms of online public opinion dissemination, this study provides insights into attenuating the negative impact of online public opinion and creating a favorable ecological space for online public opinion.

This research employs bibliometric analysis and CiteSpace software to analyze 302 Chinese articles published from 2006 to 2023 in the China National Knowledge Infrastructure (CNKI) database and 276 English articles published from 1994 to 2023 in the Web of Science core set database. Through literature keyword clustering, co-citation analysis and burst terms analysis, this paper summarizes the core scientific research institutions, scholars, hot topics and evolutionary paths of online public opinion crisis management research from both Chinese and international academic communities.

The results show that the study of online public opinion crisis management in China and internationally is centered on the life cycle theory, which integrates knowledge from information, computer and system sciences. Although there are differences in political interaction and stage evolution, the overall evolutionary path is similar, and it develops dynamically in the “benign conflict” between the expansion of the research perspective and the gradual refinement of research granularity.

This study summarizes the research results of online public opinion crisis management from China and the international academic community and identifies current research hotspots and theoretical evolution paths. Future research can focus on deepening the basic theories of public opinion crisis management under the influence of frontier technologies, exploring the subjectivity and emotionality of web users using fine algorithms and promoting the international development of network public opinion crisis management theory through transnational comparison and international cooperation.

In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation.

On a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records.

The best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval.

The paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems.

Attentive to task-related information is the prerequisite for task completion. Comparing the cognition between attentive readers (AR) and inattentive readers (IAR) is of great value for improving reading services which has seldom been studied. To explore their cognitive differences, this study investigates the effectiveness, efficiency and cognitive resource allocation strategy by eye-tracking technology.

A controlled user study of two types of task, fact-finding (FF) and content understanding (CU) tasks was conducted to collect data including answer for task, fixation duration (FD), fixation count (FC), fixation duration proportion (FDP), and fixation count proportion (FCP). 24 participants were placed into AR or IAR group according to their fixation duration on paragraphs related to task.

Two types of cognitive resource allocation strategies, question-oriented (QO) and navigation-assistant (NA) were identified according to the differences in FDP and FCP. In FF task, although QO strategy was applied by the two groups, AR group was significantly more effective and efficient. In CU task, although the two groups were similar in effectiveness and efficiency, AR group promoted their strategies to NA while IAR group sticked to applying QO strategy. Furthermore, an interesting phenomenon “win by uncertainty”, which implies IAR group may get correct answer through uncertain means, such as clue, domain knowledge or guess, rather than task-related information, was observed.

This study takes a deep insight into cognition from the prospect of attentive and inattentive to task-related information. Identifying indicators about cognition helps to distinguish attentive and inattentive readers in various tasks automatically. The cognitive resource allocation strategy applied by readers sheds new light on reading skill training. A typical reading phenomenon “win by uncertainty” was found and defined. Understanding the phenomenon is of great value for satisfying reader information need and enhancing their deep learning.

This study aims to conduct a pertinent assessment of the concept of business competitiveness and how Portugal can progress in that field, for the sake of becoming a more sustainable and wealth-creator economy.

The research was elaborated with 65 in-depth interviews with expert persons from the Portuguese business ecosystem, who were asked to reflect on the state of the economy and competitiveness of the country.

There is much room for improvement in almost all areas of activity, in particular by promoting an innovative, value-adding and exporting private sector and a lighter and more efficient public sector. The conclusions point to modernisation of the Portuguese economy as a way of making it more competitive in a highly competitive and demanding global scenario.

To the best of the authors’ knowledge, it is the first time that a reflection with experts of the local Portuguese economy has been carried out, especially after a difficult period of COVID.

The purpose of this study is to identify the research fronts by analysing highly cited core papers adjusted with the age of a paper in library and information science (LIS) where natural language processing (NLP) is being applied significantly.

By excavating international databases, 3,087 core papers that received at least 5% of the total citations have been identified. By calculating the average mean years of these core papers, and total citations received, a CPT (citation/publication/time) value was calculated in all 20 fronts to understand how a front is relatively receiving greater attention among peers within a course of time. One theme article has been finally identified from each of these 20 fronts.

Bidirectional encoder representations from transformers with CPT value 1.608 followed by sentiment analysis with CPT 1.292 received highest attention in NLP research. Columbia University New York, in terms of University, Journal of the American Medical Informatics Association, in terms of journals, USA followed by People Republic of China, in terms of country and Xu, H., University of Texas, in terms of author are the top in these fronts. It is identified that the NLP applications boost the performance of digital libraries and automated library systems in the digital environment.

Any research fronts that are identified in the findings of this paper may be used as a base for researchers who intended to perform extensive research on NLP.

To the best of the authors’ knowledge, the methodology adopted in this paper is the first of its kind where meta-analysis approach has been used for understanding the research fronts in sub field like NLP for a broad domain like LIS.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

This study aims to examine the influence of reward personalization on financing outcomes in the Industry 5.0 era, where reward-based crowdfunding meets the personalized needs of individuals.

The study utilizes a corpus of 218,822 crowdfunding projects and 1,276,786 reward options on Kickstarter to investigate the effect of reward personalization on investors’ willingness to participate in crowdfunding. The research draws on expectancy theory and employs quantitative and qualitative approaches to measure reward personalization. Quantitatively, the number of reward options is calculated by frequency; whereas text-mining techniques are implemented qualitatively to extract novelty, which serves as a proxy for innovation.

Findings indicate that reward personalization has an inverted U-shaped effect on investors’ willingness to participate, with investors in life-related projects having a stronger need for reward personalization than those interested in art-related projects. The pledge goal and reward text readability have an inverted U-shaped moderating effect on reward personalization from the perspective of reward expectations and reward instrumentality.

This study refines the application of expectancy theory to online financing, providing theoretical insight and practical guidance for crowdfunding platforms and financiers seeking to promote sustainable development through personalized innovation.

Together, learning objects (LOs) and e-pedagogical practices have the potential to improve the performance of e-learning systems in several ways. They can make e-learning more personalised and adaptable, providing students with a more engaging and effective learning experience.

The development of LOs and e-pedagogical practices have significantly influenced and changed the performance of e-learning systems. LOs are self-contained, reusable units of instructional content that create instructional materials, such as online courses, tutorials and assessments. They provide a flexible and modular approach to designing and delivering e-learning content, allowing educators to easily customise and adapt their materials to the needs of their students. e-pedagogical practices refer to the use of technology to enhance and support the teaching and learning process. They include strategies such as online collaboration, gamification and adaptive learning to improve student engagement, motivation and achievement.

To achieve this objective, this study consists of two main phases. First, the authors extract metadata from LOs using latent semantic analysis algorithms, which are considered a strong tool in web-mining exploration techniques. Second, they identify LOs according to a particular form of similarity using fuzzy c-means (FCM) algorithms. To improve classification accuracy, the FCM is used as a clustering algorithm.

Finally, in order to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset are conducted. The results of this study indicate that the proposed approach exceeds the traditional approach and produces good results.

In recent years, Chinese sentiment analysis has made great progress, but the characteristics of the language itself and downstream task requirements were not explored thoroughly. It is not practical to directly migrate achievements obtained in English sentiment analysis to the analysis of Chinese because of the huge difference between the two languages.

In view of the particularity of Chinese text and the requirement of sentiment analysis, a Chinese sentiment analysis model integrating multi-granularity semantic features is proposed in this paper. This model introduces the radical and part-of-speech features based on the character and word features, with the application of bidirectional long short-term memory, attention mechanism and recurrent convolutional neural network.

The comparative experiments showed that the F1 values of this model reaches 88.28 and 84.80 per cent on the man-made dataset and the NLPECC dataset, respectively. Meanwhile, an ablation experiment was conducted to verify the effectiveness of attention mechanism, part of speech, radical, character and word factors in Chinese sentiment analysis. The performance of the proposed model exceeds that of existing models to some extent.

The academic contribution of this paper is as follows: first, in view of the particularity of Chinese texts and the requirement of sentiment analysis, this paper focuses on solving the deficiency problem of Chinese sentiment analysis under the big data context. Second, this paper borrows ideas from multiple interdisciplinary frontier theories and methods, such as information science, linguistics and artificial intelligence, which makes it innovative and comprehensive. Finally, this paper deeply integrates multi-granularity semantic features such as character, word, radical and part of speech, which further complements the theoretical framework and method system of Chinese sentiment analysis.

With the ongoing industrial transformation of the Japanese sake industry and the continuous growth of exports in recent years, terroir, one of the core concepts in the wine culture, has been strategically used in the sake industry. Therefore, as an essential investigation, the purpose of this study is to elucidate when, how and why terroir has been used in the sake industry. This study starts with the research question: When, how and why has terroir come to be used strategically in the sake industry?

This study investigates the use of terroir in the Japanese sake industry, examining all 196 newspapers that referenced terroir from 1998 to 2022, sourced from the renowned newspaper database Nikkei Telecom 21. This study’s outcomes have been visualized through categorization work and text mining.

In this study, the use of terroir in the Japanese sake industry has gained significant momentum since 2015, with a remarkable surge observed in the 2020s. With the continuous growth in sake exports, industry players such as sake brewers are strategically structuring terroir to reinforce the authenticity of the brewing process, emphasizing the uniqueness of natural elements, such as water, sake rice and the natural environment. These findings highlight the critical role of terroir in the Japanese sake industry’s added value expansion.

This study provides objective insights regarding the recent industrial transformation for the practical sake industry, such as sake exporters and distributors. Additionally, this study enables the wine industry’s audience to understand the sake industry’s evolution in terms of wine culture.