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Article
Publication date: 16 September 2021

Sireesha Jasti

Internet has endorsed a tremendous change with the advancement of the new technologies. The change has made the users of the internet to make comments regarding the…

Abstract

Purpose

Internet has endorsed a tremendous change with the advancement of the new technologies. The change has made the users of the internet to make comments regarding the service or product. The Sentiment classification is the process of analyzing the reviews for helping the user to decide whether to purchase the product or not.

Design/methodology/approach

A rider feedback artificial tree optimization-enabled deep recurrent neural networks (RFATO-enabled deep RNN) is developed for the effective classification of sentiments into various grades. The proposed RFATO algorithm is modeled by integrating the feedback artificial tree (FAT) algorithm in the rider optimization algorithm (ROA), which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of term frequency-inverse document frequency (TF-IDF) features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted. The metrics employed for the evaluation in the proposed RFATO algorithm are accuracy, sensitivity, and specificity.

Findings

By using the proposed RFATO algorithm, the evaluation metrics such as accuracy, sensitivity and specificity are maximized when compared to the existing algorithms.

Originality/value

The proposed RFATO algorithm is modeled by integrating the FAT algorithm in the ROA, which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of TF-IDF features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted.

Details

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

Keywords

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Article
Publication date: 20 August 2021

Heesun Chung, Bum-Joon Kim, Eugenia Y. Lee and Hee-Yeon Sunwoo

This study aims to examine whether debt financing creates incentives for private firms to engage in earnings management via classification shifting. Especially, the…

Abstract

Purpose

This study aims to examine whether debt financing creates incentives for private firms to engage in earnings management via classification shifting. Especially, the authors examine whether debt-induced financial reporting incentives differ depending on the type of debt (i.e. public bonds versus private loans) and whether such incentives are influenced by the characteristics of external auditors (i.e. initial audits and auditor size).

Design/methodology/approach

The study uses data on 93,427 Korean private firms from 2001 to 2016. Classification shifting is measured by the positive correlation between non-core expenses and unexpected core earnings estimated with ordinary least squares.

Findings

The empirical analyses reveal that private firms engage in classification shifting as do public firms. Importantly, classification shifting is observed only in private firms that have outstanding debt, but not in private firms without debt. Among debt-financing private firms, classification shifting is more prevalent for firms that issue public debt than for firms that only use private debt. In addition, classification shifting of debt-financing private firms is more successful when they are audited by new auditors that are one of the non-Big 4 firms.

Research limitations/implications

The study provides evidence of classification shifting in private firms, which is novel to the literature. However, the inferences in the study depend on the validity of the model for detecting classification shifting.

Practical implications

This study helps lenders enhance their understanding on the financial reporting behaviors of borrowing firms. The results in this study suggest that lenders should be cautious in using core earnings for their investment decisions.

Originality/value

This study contributes to the literature by providing novel evidence of classification shifting in private firms. In addition, the authors contribute to the literature on debt-induced incentives for financial reporting.

Details

Managerial Auditing Journal, vol. 36 no. 7
Type: Research Article
ISSN: 0268-6902

Keywords

Content available
Article
Publication date: 16 August 2021

Jan-Halvard Bergquist, Samantha Tinet and Shang Gao

The purpose of this study is to create an information classification model that is tailored to suit the specific needs of public sector organizations in Sweden.

Abstract

Purpose

The purpose of this study is to create an information classification model that is tailored to suit the specific needs of public sector organizations in Sweden.

Design/methodology/approach

To address the purpose of this research, a case study in a Swedish municipality was conducted. Data was collected through a mixture of techniques such as literature, document and website review. Empirical data was collected through interviews with 11 employees working within 7 different sections of the municipality.

Findings

This study resulted in an information classification model that is tailored to the specific needs of Swedish municipalities. In addition, a set of steps for tailoring an information classification model to suit a specific public organization are recommended. The findings also indicate that for a successful information classification it is necessary to educate the employees about the basics of information security and classification and create an understandable and unified information security language.

Practical implications

This study also highlights that to have a tailored information classification model, it is imperative to understand the value of information and what kind of consequences a violation of established information security principles could have through the perspectives of the employees.

Originality/value

It is the first of its kind in tailoring an information classification model to the specific needs of a Swedish municipality. The model provided by this study can be used as a tool to facilitate a common ground for classifying information within all Swedish municipalities, thereby contributing the first step toward a Swedish municipal model for information classification.

Details

Information & Computer Security, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2056-4961

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Article
Publication date: 20 August 2021

Kathirvel Selvaraju and Punniyamoorthy Murugesan

The purpose of this article is to develop a cost-effective model for Multi-Criteria ABC Inventory Classification and to measure its performance in comparison to the other…

Abstract

Purpose

The purpose of this article is to develop a cost-effective model for Multi-Criteria ABC Inventory Classification and to measure its performance in comparison to the other existing models.

Design/methodology/approach

Particle Swarm Optimization (PSO) algorithm is exclusively designed for Multi-Criteria ABC Inventory Classification wherein the inventory is classified based on the objective of cost minimization, which is achieved through the inventory performance index – total relevant cost. Effectiveness of classification of the proposed model and the other classification models toward two inventory performance measures, that is, cost and inventory turnover has been computed, and the results of all models are relatively compared by arriving at the cumulative performance score of each model.

Findings

This study reveals that the ABC Inventory classification based on the proposed PSO approach is more effective toward cost and inventory turnover ratio in comparison to the twenty existing models.

Practical implications

The proposed model can be easily adapted to the industrial requirement of inventory classification by cost as objective as well as other inventory management performance measures.

Originality/value

The conceptual model is more versatile which can be adapted for various objectives and the effectiveness of classification in comparison to the other models can be measured toward each objective as well as combining all the objectives.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

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Article
Publication date: 29 June 2021

Daejin Kim, Hyoung-Goo Kang, Kyounghun Bae and Seongmin Jeon

To overcome the shortcomings of traditional industry classification systems such as the Standard Industrial Classification Standard Industrial Classification, North…

Abstract

Purpose

To overcome the shortcomings of traditional industry classification systems such as the Standard Industrial Classification Standard Industrial Classification, North American Industry Classification System North American Industry Classification System, and Global Industry Classification Standard Global Industry Classification Standard, the authors explore industry classifications using machine learning methods as an application of interpretable artificial intelligence (AI).

Design/methodology/approach

The authors propose a text-based industry classification combined with a machine learning technique by extracting distinguishable features from business descriptions in financial reports. The proposed method can reduce the dimensions of word vectors to avoid the curse of dimensionality when measuring the similarities of firms.

Findings

Using the proposed method, the sample firms form clusters of distinctive industries, thus overcoming the limitations of existing classifications. The method also clarifies industry boundaries based on lower-dimensional information. The graphical closeness between industries can reflect the industry-level relationship as well as the closeness between individual firms.

Originality/value

The authors’ work contributes to the industry classification literature by empirically investigating the effectiveness of machine learning methods. The text mining method resolves issues concerning the timeliness of traditional industry classifications by capturing new information in annual reports. In addition, the authors’ approach can solve the computing concerns of high dimensionality.

Details

Internet Research, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1066-2243

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Article
Publication date: 16 July 2021

Hilary Yerbury, Simon Darcy, Nina Burridge and Barbara Almond

Classification schemes make things happen. The Australian Disability Discrimination Act (DDA), which derives its classification system from the World Health Organization's…

Abstract

Purpose

Classification schemes make things happen. The Australian Disability Discrimination Act (DDA), which derives its classification system from the World Health Organization's International Classification of Functioning, Disability and Health (ICF), legislates for adjustments to support the inclusion of people with disability. This study explores how students with disability enrolled in a university experience the systems intended to facilitate their studying “on the same basis” as students without disability.

Design/methodology/approach

Through an online questionnaire and interviews comprising open and closed questions made available to students registered with the disability services unit of a university and follow-up interviews with a small number of students, students’ views of their own disability and effects on their participation in learning were gathered, alongside reports of their experiences of seeking support in their learning. Interview data and responses to open-ended questions were analysed using a priori and emergent coding.

Findings

The findings demonstrate that students are aware of the workings of the classification scheme and that most accept them. However, some students put themselves outside of the scheme, often as a way to exercise autonomy or to assert their “ability”, while others are excluded from it by the decisions of academic staff. Thus, the principles of fairness and equity enshrined in legislation and policy are weakened.

Originality/value

Through the voices of students with disability, it is apparent that, even though a student's classification according to the DDA and associated university policy remains constant, the outcomes of the workings of the scheme may reveal inconsistencies, emerging from the complexity of bureaucracy, processes and the exercises of power.

Details

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

Keywords

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Article
Publication date: 1 December 2002

Jeong‐Hyen Kim and Kyung‐Ho Lee

This paper reports on the design of a knowledge base for an automatic classification in the library science field, by using the facet classification principles of colon…

Abstract

This paper reports on the design of a knowledge base for an automatic classification in the library science field, by using the facet classification principles of colon classification (CC). To do so, by designing and constructing a knowledge base that is able to be classified automatically, and by inputting titles or key words of volumes into the computer, it aims to create class numbers automatically through automatic subject recognition and processing of key words in titles through the facet combination method of CC. Especially, the knowledge base for classification was designed along with the principle of globe and cylinder, automatic classification which can be possible.

Details

The Electronic Library, vol. 20 no. 6
Type: Research Article
ISSN: 0264-0473

Keywords

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Article
Publication date: 25 April 2008

Birger Hjørland

The purpose of this paper is to provide an answer to a critique put forward by Szostak against a paper written by the present author.

Abstract

Purpose

The purpose of this paper is to provide an answer to a critique put forward by Szostak against a paper written by the present author.

Design/methodology/approach

The paper is based on a literature‐based conceptual analysis based on Hjørland and Nissen Pedersen and Szostak. The main points in a core theory of classification are outlined and Szostak's criticism is examined and answered.

Findings

The paper demonstrates theoretical differences between the views adduced by Hjørland and Nissen Pedersen on the one side and by Szostak on the other.

Practical implications

Theoretical clarification is important for the future development of the field.

Originality/value

The paper should be seen as one among others developing an argument for a theoretical foundation of classification informed by the theory of knowledge.

Details

Journal of Documentation, vol. 64 no. 3
Type: Research Article
ISSN: 0022-0418

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Article
Publication date: 2 October 2017

Craig R. Scott and SoeYoon Choi

The emerging area of message classification is one of growing relevance to a wide range of organizational communicators as a variety of non-state organizations and their…

Abstract

Purpose

The emerging area of message classification is one of growing relevance to a wide range of organizational communicators as a variety of non-state organizations and their members increasingly use and misuse various terms to restrict their communication. This includes formal classifications for data security, financial/knowledge management, human resources, and other functions as well as those used informally by organizational members. Especially in a data-rich environment where our word-processing programs, e-mail tools, and other technologies afford us opportunities to engage in classification, a wide range of people at all organizational levels may serve as custodians of their own data and thus have the ability (as well as perhaps the need) to classify messages in various ways. The purpose of this paper is to describe key classification terms ranging from those found in government (e.g. top secret, confidential) to those in the private sector (e.g. business use only, trademarked) to an even wider set of terms used informally by organizational members (e.g. personal, preliminary). The growing use of message classifications will likely create various challenges and opportunities for organizations, their members, and the broader public/society. A set of future research questions is offered for corporate communication researchers and practitioners, who are well positioned to examine this emerging phenomenon.

Design/methodology/approach

This paper draws on existing literature related to the growing use of message classifications to offer a list of classification terms and an agenda for future research.

Findings

This work describes key classification terms ranging from those found in government (e.g. top secret, confidential) to those in the private sector (e.g. business use only, trademarked) to an even wider set of terms used informally by organizational members (e.g. personal, preliminary). This expanded notion of classification will likely create various challenges and opportunities for organizations, their members, and the broader public/society.

Originality/value

The emerging area of message classification is one of growing relevance to a wide range of organizational communicators as a variety of non-state organizations and their members increasingly use and misuse various terms to restrict their communication. A set of future research questions is offered for corporate communication researchers and practitioners, who are well positioned to examine this emerging phenomenon.

Details

Corporate Communications: An International Journal, vol. 22 no. 4
Type: Research Article
ISSN: 1356-3289

Keywords

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Article
Publication date: 31 May 2021

Nahed Salem and Ahmed Maher Khafaga Shehata

The study aims to explore the classification of electronic games in Dewey decimal classification (DDC) and The Library of Congress classification (LCC) schemes.

Abstract

Purpose

The study aims to explore the classification of electronic games in Dewey decimal classification (DDC) and The Library of Congress classification (LCC) schemes.

Design/methodology/approach

The study adopted a comparative analytical method to explore the topic in both the DDC and the LCC schemes by comparing its processing method in both schemes. The study measures the extent to which both schemes succeed in allocating notations covering the topic’s literature.

Findings

The study reached several results, the most important of which are: the difference between the two main cognitive sections, to which they belong to the topic, namely, arts and recreation (700) in the DDC scheme and the geography section (G) in the LCC scheme, while they were found to share the same sub-section scheme. The two schemes do not allocate notations to address the subject of electronic games as literature and other notations that have not been embodied for electronic games themselves or in the form of a compact disc or other media.

Originality/value

As far as we know, this is the first paper that compares the treatment of video games in DDC and Library of Congress classification schemes. The study allows for understanding the difference in the treatment of topics in both schemes, which would help in the decision of the adoption of a particular classification scheme.

Details

Global Knowledge, Memory and Communication, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9342

Keywords

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