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1 – 10 of over 110000
Article
Publication date: 10 May 2022

Qiang Cao, Xian Cheng and Shaoyi Liao

How to extract useful information from a very large volume of literature is a great challenge for librarians. Topic modeling technique, which is a machine learning…

Abstract

Purpose

How to extract useful information from a very large volume of literature is a great challenge for librarians. Topic modeling technique, which is a machine learning algorithm to uncover latent thematic structures from large collections of documents, is a widespread approach in literature analysis, especially with the rapid growth of academic literature. In this paper, a comparison of topic modeling based literature analysis has been done using full texts and abstracts of articles.

Design/methodology/approach

The authors conduct a comparison study of topic modeling on full-text paper and corresponding abstract to assess the influence of the different types of documents been used as input for topic modeling. In particular, the authors use the large volumes of COVID-19 research literature as a case study for topic modeling based literature analysis. The authors illustrate the research topics, research trends and topic similarity of COVID-19 research by using Latent Dirichlet allocation (LDA) and topic visualization method.

Findings

The authors found 14 research topics for COVID-19 research. The authors also found that the topic similarity between using full-text paper and corresponding abstract is higher when more documents are analyzed.

Originality/value

First, this study contributes to the literature analysis approach. The comparison study can help us understand the influence of the different types of documents on the results of topic modeling analysis. Second, the authors present an overview of COVID-19 research by summarizing 14 research topics for it. This automated literature analysis can help specialists in the health and medical domain or other people to quickly grasp the structured morphology of the current studies for COVID-19.

Details

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

Keywords

Book part
Publication date: 23 February 2016

Gabe Ignatow, Nicholas Evangelopoulos and Konstantinos Zougris

The authors apply topic sentiment analysis (several relatively new text analysis methods) to the study of public opinion as expressed in social media by comparing…

Abstract

Purpose

The authors apply topic sentiment analysis (several relatively new text analysis methods) to the study of public opinion as expressed in social media by comparing reactions to the Trayvon Martin controversy in spring 2012 by commenters on the partisan news websites the Huffington Post and Daily Caller.

Methodology/approach

Topic sentiment analysis is a text analysis method that estimates the polarity of sentiments across units of text within large text corpora (Lin & He, 2009; Mei, Ling, Wondra, Su, & Zhai, 2007).

Findings

We apply topic sentiment analysis to public opinion as expressed in social media by comparing reactions to the Trayvon Martin controversy in spring 2012 by commenters on the partisan news websites the Huffington Post and Daily Caller. Based on studies that depict contemporary news media as an “outrage industry” that incentivizes media personalities to be controversial and polarizing (Berry & Sobieraj, 2014), we predict that high-profile commentators will be more polarizing than other news personalities and topics.

Originality/value

Results of the topic sentiment analysis support this prediction and in so doing provide partial validation of the application of topic sentiment analysis to online opinion.

Details

Communication and Information Technologies Annual
Type: Book
ISBN: 978-1-78560-785-1

Keywords

Article
Publication date: 29 April 2022

Chih-Ming Chen, Szu-Yu Ho and Chung Chang

This study aims to develop a hierarchical topic analysis tool (HTAT) based on hierarchical Latent Dirichelet allocation (hLDA) to support digital humanities research that…

Abstract

Purpose

This study aims to develop a hierarchical topic analysis tool (HTAT) based on hierarchical Latent Dirichelet allocation (hLDA) to support digital humanities research that is associated with the need of topic exploration on the Digital Humanities Platform for Mr. Lo Chia-Lun’s Writings (DHP-LCLW). HTAT can assist humanities scholars on distant reading with analysis of hierarchical text topics, through classifying time-stamped texts into multiple historical eras, conducting hierarchical topic modeling (HTM) according to the texts from different eras and presenting through visualization. The comparative network diagram is another function provided to assist humanities scholars in comparing the difference in the topics they wish to explore and to track how the concept of a topic changes over time from a particular perspective. In addition, HTAT can also provide humanities scholars with the feature to view source texts, thus having high potential to be applied in promoting the effectiveness of topic exploration due to simultaneously integrating both the topic exploration functions of distant reading and close reading.

Design/methodology/approach

This study adopts a counterbalanced experimental design to examine whether there is significant differences in the effectiveness of topic inquiry, the number of relevant topics inquired and the time spent on them when research participants were alternately conducting text exploration using DHP-LCLW with HTAT or DHP-LCLW with Single-layer Topic Analysis Tool (SLTAT). A technology acceptance questionnaire and semi-structured interviews were also conducted to understand the research participants' perception and feelings toward using the two different tools to assist topic inquiry.

Findings

The experimental results show that DHP-LCLW with HTAT could better assist the research participants, in comparison with DHP-LCLW with SLTAT, to grasp the topic context of the texts from two particular perspectives assigned by this study within a short period. In addition, the results of the interviews revealed that DHP-LCLW with HTAT, in comparison with SLTAT, was able to provide a topic terms that better met research participnats' expectations and needs, and effectively guided them to the corresponding texts for close reading. In the analysis of technology acceptance and interview data, it can be found that the research participants have a high and positive tendency toward using DHP-LCLW with HTAT to assist topic inquiry.

Research limitations/implications

The Jieba Chinese word segmentation system was used in the Mr. Lo Chia-Lun’s Writings Database in this study, to perform word segmentation on Mr. Lo Chia-Lun’s writing texts for topic modeling based on hLDA. Since Jieba word segmentation system is a lexicon based word segmentation system, it cannot identify new words that have still not been collected in the lexicon well. In this case, the correctness of word segmentation on the target texts will affect the results of hLDA topic modeling, and the effectiveness of HTAT in assisting humanities scholars for topic inquiry.

Practical implications

An HTAT was developed to support digital humanities research in this study. With HTAT, DHP-LCLW provides hmanities scholars with topic clues from different hierarchical perspectives for textual exploration, and with temporal and comparative network diagrams to assist humanities scholars in tracking the evolution of the topics of specific perspectives over time, to gain a more comprehensive understanding of the overall context of the texts.

Originality/value

In recent years, topic analysis technology that can automatically extract key topic information from a large amount of texts has been developed rapidly, but the topics generated from traditional topic analysis models like LDA (Latent Dirichelet allocation) make it difficult for users to understand the differences in the topics of texts with different hierarchical levels. Thus, this study proposes HTAT which uses hLDA to build a hierarchical topic tree with a tree-like structure without the need to define the number of topics in advance, enabling humanities scholars to quickly grasp the concept of textual topics and use different hierarchical perspectives for further textual exploration. At the same time, it also provides a combination function of temporal division and comparative network diagram to assist humanities scholars in exploring topics and their changes in different eras, which helps them discover more useful research clues or findings.

Details

Aslib Journal of Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-3806

Keywords

Article
Publication date: 29 August 2022

Yue Yuan, Kan Liu and Yanli Wang

The purpose of this study is to analyze the topics of COVID-19 news articles for better obtaining the relationship among and the evolution of news topics, helping to…

Abstract

Purpose

The purpose of this study is to analyze the topics of COVID-19 news articles for better obtaining the relationship among and the evolution of news topics, helping to manage the infodemic from a quantified perspective.

Design/methodology/approach

To analyze COVID-19 news articles explicitly, this paper proposes a prism architecture. Based on epidemic-related news on China Daily and CNN, this paper identifies the topics of the two news agencies, elucidates the relationship between and amongst these topics, tracks topic changes as the epidemic progresses and presents the results visually and compellingly.

Findings

The analysis results show that CNN has a more concentrated distribution of topics than China Daily, with the former focusing on government-related information, and the latter on medical. Besides, the pandemic has had a big impact on CNN and China Daily's reporting preference. The evolution analysis of news topics indicates that the dynamic changes of topics have a strong relationship with the pandemic process.

Originality/value

This paper offers novel perspectives to review the topics of COVID-19 news articles and provide new understandings of news articles during the initial outbreak. The analysis results expand the scope of infodemic-related studies.

Details

Aslib Journal of Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-3806

Keywords

Article
Publication date: 15 June 2021

Chao Yang, Cui Huang, Jun Su and Shutao Wang

The paper aims to explore whether topic analysis (identification of the core contents, trends and topic distribution in the target field) can be performed using a more…

Abstract

Purpose

The paper aims to explore whether topic analysis (identification of the core contents, trends and topic distribution in the target field) can be performed using a more low-cost and easily applicable method that relies on a small dataset, and how we can obtain this small dataset based on the features of the publications.

Design/methodology/approach

The paper proposes a topic analysis method based on prolific and authoritative researchers (PARs). First, the authors identify PARs in a specific discipline by considering the number of publications and citations of authors. Based on the research publications of PARs (small dataset), the authors then construct a keyword co-occurrence network and perform a topic analysis. Finally, the authors compare the method with the traditional method.

Findings

The authors found that using a small dataset (only 6.47% of the complete dataset in our experiment) for topic analysis yields relatively high-quality and reliable results. The comparison analysis reveals that the proposed method is quite similar to the results of traditional large dataset analysis in terms of publication time distribution, research areas, core keywords and keyword network density.

Research limitations/implications

Expert opinions are needed in determining the parameters of PARs identification algorithm. The proposed method may neglect the publications of junior researchers and its biases should be discussed.

Practical implications

This paper gives a practical way on how to implement disciplinary analysis based on a small dataset, and how to identify this dataset by proposing a PARs-based topic analysis method. The proposed method presents a useful view of the data based on PARs that can produce results comparable to traditional method, and thus will improve the effectiveness and cost of interdisciplinary topic analysis.

Originality/value

This paper proposes a PARs-based topic analysis method and verifies that topic analysis can be performed using a small dataset.

Details

Library Hi Tech, vol. 39 no. 4
Type: Research Article
ISSN: 0737-8831

Keywords

Article
Publication date: 26 November 2021

Soohyung Joo, Jennifer Hootman and Marie Katsurai

This study aims to explore knowledge structure and research trends in the domain of digital humanities (DH) in the recent decade. The study identified prevailing topics

Abstract

Purpose

This study aims to explore knowledge structure and research trends in the domain of digital humanities (DH) in the recent decade. The study identified prevailing topics and then, analyzed trends of such topics over time in the DH field.

Design/methodology/approach

Research bibliographic data in the area of DH were collected from scholarly databases. Multiple text mining techniques were used to identify prevailing research topics and trends, such as keyword co-occurrences, bigram analysis, structural topic models and bi-term topic models.

Findings

Term-level analysis revealed that cultural heritage, geographic information, semantic web, linked data and digital media were among the most popular topics in the recent decade. Structural topic models identified that linked open data, text mining, semantic web and ontology, text digitization and social network analysis received increased attention in the DH field.

Originality/value

This study applied existent text mining techniques to understand the research domain in DH. The study collected a large set of bibliographic text, representing the area of DH from multiple academic databases and explored research trends based on structural topic models.

Details

Journal of Documentation, vol. 78 no. 4
Type: Research Article
ISSN: 0022-0418

Keywords

Article
Publication date: 11 June 2013

Heather Lutz and Laura Birou

This paper aims to provide the results of a large‐scale survey of courses dedicated to the field of logistics in higher education. This research is unique because it…

1551

Abstract

Purpose

This paper aims to provide the results of a large‐scale survey of courses dedicated to the field of logistics in higher education. This research is unique because it represents the first large‐scale study of both undergraduate and graduate logistics courses.

Design/methodology/approach

Content analysis was performed on each syllabus to identify the actual course coverage: requirements, pedagogy and content emphasis. Content analysis is a descriptive approach to categorize data and the results may be limited by the categorizations used in analysis. This aggregated information was utilized to compare historical research findings in this area with the current skills identified as important for career success. These data provide input for gap analysis between offerings in higher education and those needs identified by practitioners.

Findings

Data gathering efforts yielded a sample of 118 logistics courses representing 77 schools and six different countries. The aggregate number of topics covered in undergraduate courses totalled 95, while graduate courses covered 81 different topics. The primary evaluation techniques include the traditional exams, projects and homework. Details regarding learning objectives and grading schema are provided along with a gap analysis between the coverage of logistics courses and the needs identified by practitioners.

Originality/value

The goal is to use these data as a means of continuous improvement in the quality and value of the educational experience. The findings are designed to foster information sharing and provide data for benchmarking efforts in the development of logistics courses and curricula in academia as well as training and development by professionals in the field of logistics.

Details

Supply Chain Management: An International Journal, vol. 18 no. 4
Type: Research Article
ISSN: 1359-8546

Keywords

Article
Publication date: 9 January 2019

Hendri Murfi, Furida Lusi Siagian and Yudi Satria

The purpose of this paper is to analyze topics as alternative features for sentiment analysis in Indonesian tweets.

Abstract

Purpose

The purpose of this paper is to analyze topics as alternative features for sentiment analysis in Indonesian tweets.

Design/methodology/approach

Given Indonesian tweets, the processes of sentiment analysis start by extracting features from the tweets. The features are words or topics. The authors use non-negative matrix factorization to extract the topics and apply a support vector machine to classify the tweets into its sentiment class.

Findings

The authors analyze the accuracy using the two-class and three-class sentiment analysis data sets. Both data sets are about sentiments of candidates for Indonesian presidential election. The experiments show that the standard word features give better accuracies than the topics features for the two-class sentiment analysis. Moreover, the topic features can slightly improve the accuracy of the standard word features. The topic features can also improve the accuracy of the standard word features for the three-class sentiment analysis.

Originality/value

The standard textual data representation for sentiment analysis using machine learning is bag of word and its extensions mainly created by natural language processing. This paper applies topics as novel features for the machine learning-based sentiment analysis in Indonesian tweets.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 12 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 14 August 2019

XiaoBo Tang, Shixuan Li, Na Gu and MingLiang Tan

This study aims to explore the repost features of microblogs acting to promote the information diffusion of government-generated content on social media.

Abstract

Purpose

This study aims to explore the repost features of microblogs acting to promote the information diffusion of government-generated content on social media.

Design/methodology/approach

This study proposes a topic−sentiment analysis using a mixed social media analytics framework to analyse the microblogs collected from the Sina Weibo accounts of 30 Chinese provincial police departments. On the basis of this analysis, this study presents the distribution of reposted microblogs and reveals the reposting characteristics of police-generated microblogs (PGMs).

Findings

The experimental results indicate that children’s safety and crime-related PGMs with a positive sentiment can achieve a high level of online information diffusion.

Originality/value

This study is novel, as it reveals the reposting features of PGMs from both a topic and sentiment perspectives, and provides new findings that can inspire users’ reposting behaviour.

Details

The Electronic Library , vol. 37 no. 4
Type: Research Article
ISSN: 0264-0473

Keywords

Open Access
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…

3100

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

1 – 10 of over 110000