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Article
Publication date: 4 June 2021

Lixue Zou, Xiwen Liu, Wray Buntine and Yanli Liu

Full text of a document is a rich source of information that can be used to provide meaningful topics. The purpose of this paper is to demonstrate how to use citation…

Abstract

Purpose

Full text of a document is a rich source of information that can be used to provide meaningful topics. The purpose of this paper is to demonstrate how to use citation context (CC) in the full text to identify the cited topics and citing topics efficiently and effectively by employing automatic text analysis algorithms.

Design/methodology/approach

The authors present two novel topic models, Citation-Context-LDA (CC-LDA) and Citation-Context-Reference-LDA (CCRef-LDA). CC is leveraged to extract the citing text from the full text, which makes it possible to discover topics with accuracy. CC-LDA incorporates CC, citing text, and their latent relationship, while CCRef-LDA incorporates CC, citing text, their latent relationship and reference information in CC. Collapsed Gibbs sampling is used to achieve an approximate estimation. The capacity of CC-LDA to simultaneously learn cited topics and citing topics together with their links is investigated. Moreover, a topic influence measure method based on CC-LDA is proposed and applied to create links between the two-level topics. In addition, the capacity of CCRef-LDA to discover topic influential references is also investigated.

Findings

The results indicate CC-LDA and CCRef-LDA achieve improved or comparable performance in terms of both perplexity and symmetric Kullback–Leibler (sKL) divergence. Moreover, CC-LDA is effective in discovering the cited topics and citing topics with topic influence, and CCRef-LDA is able to find the cited topic influential references.

Originality/value

The automatic method provides novel knowledge for cited topics and citing topics discovery. Topic influence learnt by our model can link two-level topics and create a semantic topic network. The method can also use topic specificity as a feature to rank references.

Details

Library Hi Tech, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-8831

Keywords

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Article
Publication date: 5 September 2017

Muhammad Ali Masood, Rabeeh Ayaz Abbasi, Onaiza Maqbool, Mubashar Mushtaq, Naif R. Aljohani, Ali Daud, Muhammad Ahtisham Aslam and Jalal S. Alowibdi

Tags are used to annotate resources on social media platforms. Most tag recommendation methods use popular tags, but in the case of new resources that are as yet untagged…

Abstract

Purpose

Tags are used to annotate resources on social media platforms. Most tag recommendation methods use popular tags, but in the case of new resources that are as yet untagged (the cold start problem), popularity-based tag recommendation methods fail to work. The purpose of this paper is to propose a novel model for tag recommendation called multi-feature space latent Dirichlet allocation (MFS-LDA) for cold start problem.

Design/methodology/approach

MFS-LDA is a novel latent Dirichlet allocation (LDA)-based model which exploits multiple feature spaces (title, contents, and tags) for recommending tags. Exploiting multiple feature spaces allows MFS-LDA to recommend tags even if data from a feature space is missing (the cold start problem).

Findings

Evaluation of a publicly available data set consisting of around 20,000 Wikipedia articles that are tagged on a social bookmarking website shows a significant improvement over existing LDA-based tag recommendation methods.

Originality/value

The originality of MFS-LDA lies in segregation of features for removing bias toward dominant features and in synchronization of multiple feature space for tag recommendation.

Details

Program, vol. 51 no. 3
Type: Research Article
ISSN: 0033-0337

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Article
Publication date: 14 June 2021

Brahim Dib, Fahd Kalloubi, El Habib Nfaoui and Abdelhak Boulaalam

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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

Social implications

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.

Originality/value

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.

Details

International Journal of Web Information Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1744-0084

Keywords

Content available
Article
Publication date: 19 June 2020

Jeffrey D. Kushkowski, Charles B. Shrader, Marc H. Anderson and Robert E. White

Multiple disciplines such as finance, management and economics have contributed to governance research over time. However, the full intellectual structure of the…

Abstract

Purpose

Multiple disciplines such as finance, management and economics have contributed to governance research over time. However, the full intellectual structure of the governance “field” including the exchange of knowledge across disciplines and the large variety of governance topics remains to be uncovered. To appreciate the breadth of corporate governance research, it is necessary to understand the disciplinary sources from which the research stems. This manuscript focuses on the interdisciplinary underpinnings of corporate governance research.

Design/methodology/approach

This paper employs bibliometric analysis to trace the evolution of corporate governance using articles included in the ISI Web of Science database between 1990 and 2015. Journals included in these categories encompass a full range of business disciplines and provide evidence of the multi-disciplinary nature of corporate governance. It also uncovers the topics treated by disciplines under the governance umbrella using a machine learning method called latent Dirichtlet allocation (LDA).

Findings

Corporate governance research deals with a number of strategy-related topics. Unlike strategy topics that reside in a single discipline, corporate governance crosses disciplinary boundaries and includes contributions from accounting, finance, economics, law and management. Our analysis shows that over 80% of corporate governance articles come from outside the field of management. Our LDA solution indicates that the major topics in governance research include corporate governance theory, control of family firms, executive compensation and audit committees.

Originality/value

The results illustrate that corporate governance is far more interdisciplinary than previously thought. This is an important insight for corporate governance academics and may lead to collaborative research. More importantly, this research illustrates the usefulness of LDA for investigating interdisciplinary fields. This method is easily transferable to other interdisciplinary fields and it provides a powerful alternative to existing bibliometric methods. We suggest a number of topic areas within library and information science where this method may be applied, including collection development, support for interdisciplinary faculty and basic research into emerging interdisciplinary areas.

Details

Journal of Documentation, vol. 76 no. 6
Type: Research Article
ISSN: 0022-0418

Keywords

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Article
Publication date: 19 February 2018

Debin Fang, Haixia Yang, Baojun Gao and Xiaojun Li

Discovering the research topics and trends from a large quantity of library electronic references is essential for scientific research. Current research of this kind…

Abstract

Purpose

Discovering the research topics and trends from a large quantity of library electronic references is essential for scientific research. Current research of this kind mainly depends on human justification. The purpose of this paper is to demonstrate how to identify research topics and evolution in trends from library electronic references efficiently and effectively by employing automatic text analysis algorithms.

Design/methodology/approach

The authors used the latent Dirichlet allocation (LDA), a probabilistic generative topic model to extract the latent topic from the large quantity of research abstracts. Then, the authors conducted a regression analysis on the document-topic distributions generated by LDA to identify hot and cold topics.

Findings

First, this paper discovers 32 significant research topics from the abstracts of 3,737 articles published in the six top accounting journals during the period of 1992-2014. Second, based on the document-topic distributions generated by LDA, the authors identified seven hot topics and six cold topics from the 32 topics.

Originality/value

The topics discovered by LDA are highly consistent with the topics identified by human experts, indicating the validity and effectiveness of the methodology. Therefore, this paper provides novel knowledge to the accounting literature and demonstrates a methodology and process for topic discovery with lower cost and higher efficiency than the current methods.

Details

Library Hi Tech, vol. 36 no. 3
Type: Research Article
ISSN: 0737-8831

Keywords

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Article
Publication date: 12 June 2017

San-Yih Hwang, Chih-Ping Wei, Chien-Hsiang Lee and Yu-Siang Chen

The information needs of the users of literature database systems often come from the task at hand, which is short term and can be represented as a small number of…

Abstract

Purpose

The information needs of the users of literature database systems often come from the task at hand, which is short term and can be represented as a small number of articles. Previous works on recommending articles to satisfy users’ short-term interests have utilized article content, usage logs, and more recently, coauthorship networks. The usefulness of coauthorship has been demonstrated by some research works, which, however, tend to adopt a simple coauthorship network that records only the strength of coauthorships. The purpose of this paper is to enhance the effectiveness of coauthorship-based recommendation by incorporating scholars’ collaboration topics into the coauthorship network.

Design/methodology/approach

The authors propose a latent Dirichlet allocation (LDA)-coauthorship-network-based method that integrates topic information into the links of the coauthorship networks using LDA, and a task-focused technique is developed for recommending literature articles.

Findings

The experimental results using information systems journal articles show that the proposed method is more effective than the previous coauthorship network-based method over all scenarios examined. The authors further develop a hybrid method that combines the results of content-based and LDA-coauthorship-network-based recommendations. The resulting hybrid method achieves greater or comparable recommendation effectiveness under all scenarios when compared to the content-based method.

Originality/value

This paper makes two contributions. The authors first show that topic model is indeed useful and can be incorporated into the construction of coaurthoship-network to improve literature recommendation. The authors subsequently demonstrate that coauthorship-network-based and content-based recommendations are complementary in their hit article rank distributions, and then devise a hybrid recommendation method to further improve the effectiveness of literature recommendation.

Details

Online Information Review, vol. 41 no. 3
Type: Research Article
ISSN: 1468-4527

Keywords

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Book part
Publication date: 13 December 2017

Qiongwei Ye and Baojun Ma

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…

Abstract

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.

Details

Internet+ and Electronic Business in China: Innovation and Applications
Type: Book
ISBN: 978-1-78743-115-7

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Article
Publication date: 17 April 2020

Jun Wang, Yunpeng Li, Bihu Wu and Yao Wang

The purpose of this paper is to study tourists’ spatial and psychological involvement reflected through tourism destination image (TDI), TDI is divided into on-site and…

Abstract

Purpose

The purpose of this paper is to study tourists’ spatial and psychological involvement reflected through tourism destination image (TDI), TDI is divided into on-site and after-trip groups and the two groups are compared in the frame of three-dimensional continuums.

Design/methodology/approach

By conducting latent Dirichlet allocation (LDA) modeling to tourism user-generated content, structural topic models are established. The topics separated out from unstructured raw texts are structural themes and representations of TDI. Social network analysis (SNA) reveals the quantitative and structural differences of three-dimensional continuums of the two TDI groups.

Findings

The findings reveal that from the stage of on-site to after-trip, tourist perception of TDI shifts from psychologically to functionally-oriented, from common to unique, and from holistic to more attribute focused. Also, it is suggested that from a postmodernism perspective, TDI is never unique, fixed or universal, but has different image perceptions and feedbacks for different tourists.

Research limitations/implications

With the assistance of social sensing, a panoramic view of TDI could be established. Targeted and precision destination marketing and image promotion could be applied out to each individual tourist.

Originality/value

Combining with the perspectives of the tourist-destination space system and the tourism involvement theory, this research proposes a TDI transformation model and an explanation of the internal mechanism. The originality of research also lies in the methodological innovation of social sensing data and the LDA topic model.

研究目的

本研究针对旅游目的地形象(TDI)及其体现出的游客空间和心理涉入, 将旅游目的地形象划分为在场形象和游后形象, 并将二者在TDI三维连续体(Three-dimensional continuums)框架下进行比较。

研究方法

本研究应用内容分析法, 通过对旅游用户生成内容(tourism UGC)进行LDA(Latent Dirichlet Allocation)建模, 从非结构化的原始文本中建立起结构化的语义主题模型, 并且应用社会网络分析(Social Network Analysis), 从定量和结构化的角度揭示了游中与游后目的地形象的差异。

研究发现

研究发现, 从游中到游后, 游客的目的地形象感知经历了从心理到功能、从一般到特殊、从整体到属性的转变。同时, 基于后现代主义的视角, 旅游目的地形象并不是唯一的、固定的或放之四海而皆准的, 而是在不同的游客感知中有不同的形象和体现。

研究应用

应用社会感知(Social Sensing)理论可以全面解析旅游目的地形象。同时可以针对特定游客采取精准定点的旅游目的地营销和形象推广手段。

研究价值

本研究从旅游目的地空间系统和旅游涉入理论视角出发, 提出了旅游目的地形象转变的模型和其内在机制解释, 在方法上创新性地使用了社会感知数据和LDA主题模型。

关键词

关键词 旅游目的地形象, 在场形象, 游后形象, 旅游用户生成内容 (tourism UGC), LDA(Latent Dirichlet Allocation)建模, 社会感知

Propósito

Para estudiar el grado de participación espacial y psicológica de los turistas reflejado en la imagen del destino turístico (TDI), el TDI se divide en grupo en el sitio y grupo posterior al viaje, y los dos grupos se comparan en el marco del continuo tridimensional.

Diseño/Metodología

Al modelar la posible asignación de Dirichlet (LDA) del contenido generado por el usuario turístico (UGC), se estableció un modelo de tema estructural. El tema que está separado del texto original no estructurado es el tema estructurado y la representación de TDI. El análisis de redes sociales reveló diferencias en el número y la estructura de los continuos tridimensionales de los dos grupos de TDI.

Resultados

Los resultados de la encuesta muestran que, desde la escena hasta los viajes, la percepción de los turistas de TDI cambia de orientación psicológica a funcional, de lo ordinario a lo único, y de una atención general a más. Además, se sugiere que desde una perspectiva posmoderna, TDI nunca es único, fijo o universal, sino que tiene diferentes percepciones de imagen y comentarios para diferentes visitantes.

Implicaciones practicas

Con la ayuda de la detección social, se podría establecer una vista panorámica de TDI. El marketing de destino y la promoción de imágenes dirigidos y precisos podrían aplicarse a cada turista individual.

Originalidad/valor

Combinando con las perspectivas del sistema espacial de destino turístico y la teoría de la participación turística, esta investigación propone un modelo de transformación TDI y la explicación del mecanismo interno. La originalidad de la investigación también radica en la innovación metodológica de los datos de detección social y el modelo de tema LDA.

Details

Tourism Review, vol. 76 no. 1
Type: Research Article
ISSN: 1660-5373

Keywords

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Article
Publication date: 9 January 2020

Duen-Ren Liu, Yun-Cheng Chou and Ciao-Ting Jian

Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits…

Abstract

Purpose

Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users’ satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles.

Design/methodology/approach

Association rule mining is applied to users’ news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website.

Findings

The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users’ online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance.

Originality/value

Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user’s online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.

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Article
Publication date: 24 July 2020

Thanh-Tho Quan, Duc-Trung Mai and Thanh-Duy Tran

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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).

Details

Online Information Review, vol. 44 no. 5
Type: Research Article
ISSN: 1468-4527

Keywords

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