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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

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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. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-8831

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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…

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1411

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

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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

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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

Content available
Article
Publication date: 15 July 2021

Kalervo Järvelin and Pertti Vakkari

This paper analyses the research in Library and Information Science (LIS) and reports on (1) the status of LIS research in 2015 and (2) on the evolution of LIS research…

Abstract

Purpose

This paper analyses the research in Library and Information Science (LIS) and reports on (1) the status of LIS research in 2015 and (2) on the evolution of LIS research longitudinally from 1965 to 2015.

Design/methodology/approach

The study employs a quantitative intellectual content analysis of articles published in 30+ scholarly LIS journals, following the design by Tuomaala et al. (2014). In the content analysis, we classify articles along eight dimensions covering topical content and methodology.

Findings

The topical findings indicate that the earlier strong LIS emphasis on L&I services has declined notably, while scientific and professional communication has become the most popular topic. Information storage and retrieval has given up its earlier strong position towards the end of the years analyzed. Individuals are increasingly the units of observation. End-user's and developer's viewpoints have strengthened at the cost of intermediaries' viewpoint. LIS research is methodologically increasingly scattered since survey, scientometric methods, experiment, case studies and qualitative studies have all gained in popularity. Consequently, LIS may have become more versatile in the analysis of its research objects during the years analyzed.

Originality/value

Among quantitative intellectual content analyses of LIS research, the study is unique in its scope: length of analysis period (50 years), width (8 dimensions covering topical content and methodology) and depth (the annual batch of 30+ scholarly journals).

Details

Journal of Documentation, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0022-0418

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…

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1892

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

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Book part
Publication date: 26 November 2020

Timothy R. Hannigan and Guillermo Casasnovas

Field emergence poses an intriguing problem for institutional theorists. New issue fields often arise at the intersection of different sectors, amidst extant structures of…

Abstract

Field emergence poses an intriguing problem for institutional theorists. New issue fields often arise at the intersection of different sectors, amidst extant structures of meanings and actors. Such nascent fields are fragmented and lack clear guides for action; making it unclear how they ever coalesce. The authors propose that provisional social structures provide actors with macrosocial presuppositions that shape ongoing field-configuration; bootstrapping the field. The authors explore this empirically in the context of social impact investing in the UK, 2000–2013, a period in which this field moved from clear fragmentation to relative alignment. The authors combine different computational text analysis methods, and data from an extensive field-level study, to uncover meaningful patterns of interaction and structuration. Our results show that across various periods, different types of actors were linked together in discourse through “actor–meaning couplets.” These emergent couplings of actors and meanings provided actors with social cues, or macrofoundations, which guided their local activities. The authors thus theorize a recursive, co-constitutive process: as punctuated moments of interaction generate provisional structures of actor–meaning couplets, which then cue actors as they navigate and constitute the emerging field. Our model re-energizes the core tenets of new structuralism and contributes to current debates about institutional emergence and change.

Details

Macrofoundations: Exploring the Institutionally Situated Nature of Activity
Type: Book
ISBN: 978-1-83909-160-5

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Book part
Publication date: 10 May 2000

Mary S. Doucet, Thomas A. Doucet and Patricia A. Essex

Abstract

Details

Advances in Accounting Education Teaching and Curriculum Innovations
Type: Book
ISBN: 978-1-84950-872-8

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Article
Publication date: 16 April 2018

Alfredo Milani, Niyogi Rajdeep, Nimita Mangal, Rajat Kumar Mudgal and Valentina Franzoni

This paper aims to propose an approach for the analysis of user interest based on tweets, which can be used in the design of user recommendation systems. The extract topics

Downloads
277

Abstract

Purpose

This paper aims to propose an approach for the analysis of user interest based on tweets, which can be used in the design of user recommendation systems. The extract topics are seen positively by the user.

Design/methodology/approach

The proposed approach is based on the combination of sentiment extraction and classification analysis of tweet to extract the topic of interest. The proposed hybrid method is original. The topic extraction phase uses a method based on semantic distance in the WordNet taxonomy. Sentiment extraction uses NLPcore.

Findings

The algorithm has been extensively tested using real tweets generated by 1,000 users. The results are quite encouraging and outperform state-of-the-art results and confirm the suitability of the approach combining sentiment and categorization for the topic of interest extraction.

Research limitations/implications

The hybrid method combining sentiment extraction and classification for user positive topics represents a novel contribution with many potential applications.

Practical implications

The functionality of positive topic extraction is very useful as a component in the design of a recommender system based on user profiling from Twitter user behaviors.

Social implications

The application of the proposed method in short-text social network can be massive and beyond the applications in tweets.

Originality/value

There are few works that have considered both sentiment analysis and classification to find out users’ interest. The algorithm has been extensively tested using real tweets generated by 1,000 users. The results are quite encouraging and outperform state-of-the-art results.

Details

International Journal of Web Information Systems, vol. 14 no. 1
Type: Research Article
ISSN: 1744-0084

Keywords

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