Development of a Job Applicants E-government System Based on Web Mining Classification Methods

Governmental establishments are maintaining historical data for job applicants for future analysis of predication, improvement of benefits, profits, and development of organizations and institutions. In e-government, a decision can be made about job seekers after mining in their information that will lead to a beneficial insight. This paper proposes the development and implementation of an applicant's appropriate job prediction system to suit his or her skills using web content classification algorithms (Logit Boost, j48, PART, Hoeffding Tree, Naive Bayes). Furthermore, the results of the classification algorithms are compared based on data sets called "job classification data" sets. Experimental results indicated that the algorithm j48 had the highest precision (94.80%) compared to other algorithms for the aforementioned dataset.

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Performance Comparison of Data Mining Classifiers on Web Log Data

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9349 ◽

2020 ◽

Vol 17 (11) ◽

pp. 5113-5116

Author(s):

Varun Malik ◽

Vikas Rattan ◽

Jaiteg Singh ◽

Ruchi Mittal ◽

Urvashi Tandon

Keyword(s):

Web Mining ◽

Performance Comparison ◽

Web Usage Mining ◽

Classification Algorithms ◽

Web Content ◽

Web Usage ◽

Web Structure ◽

Web Structure Mining ◽

Content Mining ◽

The Web

Web usage mining is the branch of web mining that deals with mining of data over the web. Web mining can be categorized as web content mining, web structure mining, web usage mining. In this paper, we have summarized the web usage mining results executed over the user tool WMOT (web mining optimized tool) based on the WEKA tool that has been used to apply various classification algorithms such as Naïve Bayes, KNN, SVM and tree based algorithms. Authors summarized the results of classification algorithms on WMOT tool and compared the results on the basis of classified instances and identify the algorithms that gives better instances accuracy.

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WEB MINING: A SURVEY OF CURRENT RESEARCH, TECHNIQUES, AND SOFTWARE

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622008003150 ◽

2008 ◽

Vol 07 (04) ◽

pp. 683-720 ◽

Cited By ~ 47

Author(s):

QINGYU ZHANG ◽

RICHARD S. SEGALL

Keyword(s):

Web Mining ◽

Research Work ◽

Data Sets ◽

Web Content ◽

Information Selection ◽

Future Directions ◽

Web Structure ◽

Web Structure Mining ◽

Content Mining ◽

Key Issues

The purpose of this paper is to provide a more current evaluation and update of web mining research and techniques available. Current advances in each of the three different types of web mining are reviewed in the categories of web content mining, web usage mining, and web structure mining. For each tabulated research work, we examine such key issues as web mining process, methods/techniques, applications, data sources, and software used. Unlike previous investigators, we divide web mining processes into the following five subtasks: (1) resource finding and retrieving, (2) information selection and preprocessing, (3) patterns analysis and recognition, (4) validation and interpretation, and (5) visualization. This paper also reports the comparisons and summaries of selected software for web mining. The web mining software selected for discussion and comparison in this paper are SPSS Clementine, Megaputer PolyAnalyst, ClickTracks by web analytics, and QL2 by QL2 Software Inc. Applications of these selected web mining software to available data sets are discussed together with abundant presentations of screen shots, as well as conclusions and future directions of the research.

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Comparison of the accuracy of classification algorithms on three data-sets in data mining: Example of 20 classes

International Journal of Engineering Science and Technology ◽

10.4314/ijest.v12i3.8 ◽

2020 ◽

Vol 12 (3) ◽

pp. 81-89

Author(s):

T. Sanlı ◽

Ç. Sıcakyüz ◽

O.H. Yüregir

Keyword(s):

Data Mining ◽

Text Mining ◽

Web Mining ◽

Industrial Engineering ◽

Classification Algorithms ◽

Data Sets ◽

Data Set ◽

Data Mining Algorithms ◽

Clustering And Classification ◽

Doctoral Dissertations

Data mining, which has different uses such as text mining and web mining, is especially used for clustering and classification purposes. In this study, this method was used for both classification and text mining. The aim of the study was the assessment of the performances of the data mining algorithms on the three datasets. A total of 6631 master's and doctoral dissertations written in the field of industrial engineering were downloaded from the Higher Education Council database. With the help of summary, subject titles and keywords of these dissertations, it was tried to be guessed which sub-field of industrial engineering it belongs to using WEKA program. As a result, it was observed that the data set containing the keywords obtained by weighting the expert opinion was more successful than the other two data sets. And the three most successful classification algorithms were found to be kNN, SMO, and J48, respectively. Keywords: Classification Algorithms, Data Mining, Multiple Classes, Dataset.

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Language change across a lifetime: A historical micro-perspective

Linguistics Vanguard ◽

10.1515/lingvan-2020-0029 ◽

2021 ◽

Vol 7 (s2) ◽

Author(s):

Alexander Bergs

Keyword(s):

Communities Of Practice ◽

Social Factors ◽

Language Use ◽

Language Change ◽

Historical Data ◽

Data Sets ◽

The Social ◽

Challenges And Opportunities ◽

Linguistic Behavior ◽

Micro Analysis

Abstract This paper focuses on the micro-analysis of historical data, which allows us to investigate language use across the lifetime of individual speakers. Certain concepts, such as social network analysis or communities of practice, put individual speakers and their social embeddedness and dynamicity at the center of attention. This means that intra-speaker variation can be described and analyzed in quite some detail in certain historical data sets. The paper presents some exemplary empirical analyses of the diachronic linguistic behavior of individual speakers/writers in fifteenth to seventeenth century England. It discusses the social factors that influence this behavior, with an emphasis on the methodological and theoretical challenges and opportunities when investigating intra-speaker variation and change.

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Semantic Web mining for Content-Based Online Shopping Recommender Systems

International Journal of Intelligent Information Technologies ◽

10.4018/ijiit.2019100103 ◽

2019 ◽

Vol 15 (4) ◽

pp. 41-56 ◽

Cited By ~ 1

Author(s):

Ibukun Tolulope Afolabi ◽

Opeyemi Samuel Makinde ◽

Olufunke Oyejoke Oladipupo

Keyword(s):

Semantic Web ◽

Recommender Systems ◽

Web Mining ◽

Online Shopping ◽

Semantic Analysis ◽

Second Phase ◽

Web Content ◽

Textual Data ◽

Content Mining ◽

Bayes Algorithm

Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.

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Multi-part Representation Learning For Cross-domain Web Content Classification Using Neural Networks

Proceedings of the 28th ACM Conference on Hypertext and Social Media - HT '17 ◽

10.1145/3078714.3078745 ◽

2017 ◽

Author(s):

Ganesh J ◽

Himanshu Sharad Bhatt ◽

Manjira Sinha ◽

Shourya Roy

Keyword(s):

Neural Networks ◽

Representation Learning ◽

Web Content ◽

Cross Domain ◽

Content Classification

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Enhancing transparency through open government data: the case of data portals and their features and capabilities

Online Information Review ◽

10.1108/oir-05-2020-0204 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Martin Lněnička ◽

Renata Machova ◽

Jolana Volejníková ◽

Veronika Linhartová ◽

Radka Knezackova ◽

...

Keyword(s):

Open Data ◽

Open Government ◽

Data Sets ◽

Web Content ◽

Content Type ◽

Domain Experts ◽

Computer Mediated ◽

Open Government Data ◽

Decision Making Processes ◽

Government Data

PurposeThe purpose of this paper was to draw on evidence from computer-mediated transparency and examine the argument that open government data and national data infrastructures represented by open data portals can help in enhancing transparency by providing various relevant features and capabilities for stakeholders' interactions.Design/methodology/approachThe developed methodology consisted of a two-step strategy to investigate research questions. First, a web content analysis was conducted to identify the most common features and capabilities provided by existing national open data portals. The second step involved performing the Delphi process by surveying domain experts to measure the diversity of their opinions on this topic.FindingsIdentified features and capabilities were classified into categories and ranked according to their importance. By formalizing these feature-related transparency mechanisms through which stakeholders work with data sets we provided recommendations on how to incorporate them into designing and developing open data portals.Social implicationsThe creation of appropriate open data portals aims to fulfil the principles of open government and enables stakeholders to effectively engage in the policy and decision-making processes.Originality/valueBy analyzing existing national open data portals and validating the feature-related transparency mechanisms, this paper fills this gap in existing literature on designing and developing open data portals for transparency efforts.

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Study from Korea shows strong CSR policies significantly increase job seekers' application intentions

Human Resource Management International Digest ◽

10.1108/hrmid-08-2021-0171 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Keyword(s):

Real Life ◽

Value Congruence ◽

Job Applicants ◽

Job Seekers ◽

Content Type ◽

Mediated Moderation ◽

Mediating Role ◽

Corporate Social ◽

The Relationship

Purpose This study investigated how and when corporate social responsibility (CSR) fosters job seekers’ application intentions. The authors used a “mediated moderation mode” to explore the positive effect of CSR on job seekers’ intention to apply. They considered the moderating role of applicants’ calling and the mediating role of value congruence in the relationship between the person and organization. Design/methodology/approach To test their hypotheses the authors developed a questionnaire and sent it to a sample of 259 college students with a mean age of 22.67 in South Korea. All were either prospective or current job seekers and 55.2pc were female. Two scenarios were developed based on the real-life case of a well-known coffee franchise’s CSR policies. The scenarios were identical except that one had more proactive CSR policies. Findings Results showed that a company’s proactive CSR programs increase job seekers’ intention to apply, which was moderated by their “calling” for the job. The research also demonstrated that “value congruence” between the applicant and the organization fully mediated the interaction between CSR and calling. The results, the authors said, suggested that engaging in active CSR could attract job applicants, providing a potential competitive advantage. Originality/value The authors said their study contributed to the literature as it took the job seeker’s perspective whereas most previous research on calling focused on employees. They said it was the first study to empirically demonstrate the interaction between a sense of calling and CSR.

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