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The purpose of this paper is to explore and describe research presented in the International Journal of Quality & Reliability Management (IJQRM), thereby creating an increased understanding of how the areas of research have evolved through the years. An additional purpose is to show how text mining methodology can be used as a tool for exploration and description of research publications.

The study applies text mining methodologies to explore and describe the digital library of IJQRM from 1984 up to 2014. To structure and condense the data, k-means clustering and probabilistic topic modeling with latent Dirichlet allocation is applied. The data set consists of research paper abstracts.

The results support the suggestion of the occurrence of trends, fads and fashion in research publications. Research on quality function deployment (QFD) and reliability management are noted to be on the downturn whereas research on Six Sigma with a focus on lean, innovation, performance and improvement on the rise. Furthermore, the study confirms IJQRM as a scientific journal with quality and reliability management as primary areas of coverage, accompanied by specific topics such as total quality management, service quality, process management, ISO, QFD and Six Sigma. The study also gives an insight into how text mining can be used as a way to efficiently explore and describe large quantities of research paper abstracts.

The study focuses on abstracts of research papers, thus topics and categories that could be identified via other journal publications, such as book reviews; general reviews; secondary articles; editorials; guest editorials; awards for excellence (notifications); introductions or summaries from conferences; notes from the publisher; and articles without an abstract, are excluded.

There do not seem to be any prior text mining studies that apply cluster modeling and probabilistic topic modeling to research article abstracts in the IJQRM. This study therefore offers a unique perspective on the journal’s content.

This paper aims to determine if the digital humanities technique of topic modeling would reveal interesting patterns in a corpus of library-themed literature focused on the future of libraries and pioneer a collaboration model in librarian-led digital humanities projects. By developing the project, librarians learned how to better support digital humanities by actually doing digital humanities, as well as gaining insight on the variety of approaches taken by researchers and commenters to the idea of the future of libraries.

The researchers collected a corpus of over 150 texts (articles, blog posts, book chapters, websites, etc.) that all addressed the future of the library. They ran several instances of latent Dirichlet allocation style topic modeling on the corpus using the programming language R. Once they produced a run in which the topics were cohesive and discrete, they produced word-clouds of the words associated with each topic, visualized topics through time and examined in detail the top five documents associated with each topic.

The research project provided an effective way for librarians to gain practical experience in digital humanities and develop a greater understanding of collaborative workflows in digital humanities. By examining a corpus of library-themed literature, the researchers gained new insight into how the profession grapples with the idea of the future and an appreciation for topic modeling as a form of literature review.

Topic modeling a future-themed corpus of library literature is a unique research project and provides a way to support collaboration between library faculty and researchers from outside the library.

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.

Topic model has been widely applied to discover important information from a vast amount of unstructured data. Traditional long-text topic models such as Latent Dirichlet Allocation may suffer from the sparsity problem when dealing with short texts, which mostly come from the Web. These models also exist the readability problem when displaying the discovered topics. The purpose of this paper is to propose a novel model called the Sense Unit based Phrase Topic Model (SenU-PTM) for both the sparsity and readability problems.

SenU-PTM is a novel phrase-based short-text topic model under a two-phase framework. The first phase introduces a phrase-generation algorithm by exploiting word embeddings, which aims to generate phrases with the original corpus. The second phase introduces a new concept of sense unit, which consists of a set of semantically similar tokens for modeling topics with token vectors generated in the first phase. Finally, SenU-PTM infers topics based on the above two phases.

Experimental results on two real-world and publicly available datasets show the effectiveness of SenU-PTM from the perspectives of topical quality and document characterization. It reveals that modeling topics on sense units can solve the sparsity of short texts and improve the readability of topics at the same time.

The originality of SenU-PTM lies in the new procedure of modeling topics on the proposed sense units with word embeddings for short-text topic discovery.

The purpose of this study was to explore influences of review-related information on topical proportions and the pattern of word appearances in each topic (topical content) using structural topic model (STM).

For 173,607 Yelp.com reviews written in 2005-2016, STM-based topic modeling was applied with inclusion of covariates in addition to traditional statistical analyses.

Differences in topic prevalence and topical contents were found between certified green and non-certified restaurants. Customers’ recognition in sustainable food topics were changed over time.

This study demonstrates the application of STM for the systematic analysis of a large amount of text data.

Limited study in the hospitality literature examined the influence of review-level metadata on topic and term estimation. Through topic modeling, customers’ natural responses toward green practices were identified.

本研究旨在通过结构性话题建模（STM）方法以开拓评论性内容对于话题组成和词条构成的影响。

本论文采用 173，607 份 Yelp.com 在 2015 至 2016 年间的评论内容为样本，STM 分析结合共变量形成话题性建模。

话题趋势和话题内容的不同存在于认证过的绿色餐馆与非认证的绿色餐馆中。消费者对于可持续性的食物话题兴趣随着时间而改变。

本研究对 STM 相关大规模文本型数据的系统分析方法给与启示。

在酒店管理文献中很少有文章研究评论性元数据对于话题和词条预估的影响。通过话题建模，消费者对于绿色措施的反馈获得了梳理和确认。

This paper proposes an approach to identify categorical influencers (i.e. influencers is the person who is active in the targeted categories) in social media channels. Categorical influencers are important for media marketing but to automatically detect them remains a challenge.

We deployed the emerging deep learning approaches. Precisely, we used word embedding to encode semantic information of words occurring in the common microtext of social media and used variational autoencoder (VAE) to approximate the topic modeling process, through which the active categories of influencers are automatically detected. We developed a system known as Categorical Influencer Detection (CID) to realize those ideas.

The approach of using VAE to simulate the Latent Dirichlet Allocation (LDA) process can effectively handle the task of topic modeling on the vast dataset of microtext on social media channels.

This work has two major contributions. The first one is the detection of topics on microtexts using deep learning approach. The second is the identification of categorical influencers in social media.

This work can help brands to do digital marketing on social media effectively by approaching appropriate influencers. A real case study is given to illustrate it.

In this paper, we discuss an approach to automatically identify the active categories of influencers by performing topic detection from the microtext related to the influencers in social media channels. To do so, we use deep learning to approximate the topic modeling process of the conventional approaches (such as LDA).

The purpose of this study is to facilitate the task of finding appropriate information to read about, and searching for people who are in the same field of interest. Knowing that more people keep up with new streaming information on Twitter micro-blogging service. With the immense number of micro-posts shared via the follower/followee network graph, Twitter users find themselves in front of millions of tweets, which makes the task crucial.

In this paper, a long short–term memory (LSTM) model that relies on the latent Dirichlet allocation (LDA) output vector for followee recommendation, the LDA model applied as a topic modeling strategy is proposed.

This study trains the model using a real-life data set extracted based on Twitter follower/followee architecture. It confirms the effectiveness and scalability of the proposed approach. The approach improves the state-of-the-art models average-LSTM and time-LSTM.

This study improves mainly the existing followee recommendation systems. Because, unlike previous studies, it applied a non-hand-crafted method which is the LSTM neural network with LDA model for topics extraction. The main limitation of this study is the cold-start users cannot be treated, also some active fake accounts may not be detected.

The aim of this approach is to assist users seeking appropriate information to read about, by choosing appropriate profiles to follow.

This approach consolidates the social relationship between users in a microblogging platform by suggesting like-minded people to each other. Thus, finding users with the same interests will be easy without spending a lot of time seeking relevant users.

Instead of classic recommendation models, the paper provides an efficient neural network searching method to make it easier to find appropriate users to follow. Therefore, affording an effective followee recommendation system.

The present article's primary purpose is the topic modeling of the global coronavirus publications in the last 50 years.

The present study is applied research that has been conducted using text mining. The statistical population is the coronavirus publications that have been collected from the Web of Science Core Collection (1970–2020). The main keywords were extracted from the Medical Subject Heading browser to design the search strategy. Latent Dirichlet allocation and Python programming language were applied to analyze the data and implement the text mining algorithms of topic modeling.

The findings indicated that the SARS, science, protein, MERS, veterinary, cell, human, RNA, medicine and virology are the most important keywords in the global coronavirus publications. Also, eight important topics were identified in the global coronavirus publications by implementing the topic modeling algorithm. The highest number of publications were respectively on the following topics: “structure and proteomics,” “Cell signaling and immune response,” “clinical presentation and detection,” “Gene sequence and genomics,” “Diagnosis tests,” “vaccine and immune response and outbreak,” “Epidemiology and Transmission” and “gastrointestinal tissue.”

The originality of this article can be considered in three ways. First, text mining and Latent Dirichlet allocation were applied to analyzing coronavirus literature for the first time. Second, coronavirus is mentioned as a hot topic of research. Finally, in addition to the retrospective approaches to 50 years of data collection and analysis, the results can be exploited with prospective approaches to strategic planning and macro-policymaking.

The purpose of this paper is to explore and describe how research on quality management (QM) has evolved historically. The study includes the complete digital archive of three academic journals in the field of QM. Thereby, a unique depiction of how the general outlines of the field as well as trends in research topics have evolved through the years is presented.

The study applies cluster and probabilistic topic modeling to unstructured data from The International Journal of Quality & Reliability Management, The TQM Journal and Total Quality Management & Business Excellence. In addition, trend analysis using support vector machine is performed.

The study identifies six central, perpetual themes of QM research: control, costs, reliability and failure; service quality; TQM – implementation and performance; ISO – certification, standards and systems; Innovation, practices and learning and customers – research and product design. Additionally, historical surges and shifts in research focus are recognized in the study. From these trends, a decrease in interest in TQM and control of quality, costs and processes in favor of service quality, customer satisfaction, Six Sigma, Lean and innovation can be noted during the past decade. The results validate previous findings.

Of the identified central themes, innovation, practices and learning appears not to have been documented as a fundamental part of QM research in previous studies. Thus, this theme can be regarded as a new perspective on QM research and thereby on QM.

The purpose of this research is to analyze the shifting perceptions of international tourists to Jeju Island and provide practical lessons to the tourism industry. Specifically, in regard to three United Nations Educational, Scientific and Cultural Organization (UNESCO) natural World Heritage sites in Jeju, this research measures the most salient topics mentioned by tourists to inform a more accurate perception of the island’s most valuable natural assets as reported by tourism experiences.

This study used a Web crawler to gather over 1,500 English language reviews from international tourists from a famous travel information website. The collected data were then preprocessed for stemming and lemmatization. After this, the processed text data were analyzed through a latent Dirichlet allocation (LDA)-based topic modeling approach to identify the most prominent clusters of ideas mentioned and represent them visually through graphs, tables and charts.

The findings from this research suggest that there are ten identifiable topics. Topics focusing on “adventure,” “summits” and “winter” showed noticeable increases, whereas topics focusing on “sunrise peak” and “UNESCO” have decreased over time. There is a trend for international tourists to be ever more conscious of the adventurous and rugged aspects of Jeju, and the novelty of mentioning UNESCO status seems to have worn off. Furthermore, there is the proclivity for tourists to mention “worth” and “enjoy” more as time goes on.

This study applies LDA-based topic modeling and LDAvis using user-generated online reviews with time-series analyses. Consequently, it provides unique insights into the changing perceptions of ecotourism on Jeju today, as well as contribution to smart tourism fields.