Detecting Subjectivity in Staff Perfomance Appraisals by Using Text Mining: Teachers Appraisals of Palestinian Government Case Study

2017 ◽  
Author(s):  
Amani A. Abed ◽  
Alaa M. El-Halees
Keyword(s):  
Text Mining

Are Universities Ready to Deliver Digital Skills and Competences? A Text Mining Driven Case Study on Marketing Courses in Italy

2021 ◽  
Author(s):  
Irene Spada ◽  
Filippo Chiarello ◽  
Simone Barandoni ◽  
Gianluca Ruggi ◽  
Martini Antonella ◽  
...  
Keyword(s):  
Text Mining ◽  
Digital Skills

A framework for text mining on Twitter: a case study on joint comprehensive plan of action (JCPOA)- between 2015 and 2019

2021 ◽  
Author(s):  
Rashid Behzadidoost ◽  
Mahdieh Hasheminezhad ◽  
Mohammad Farshi ◽  
Vali Derhami ◽  
Farinaz Alamiyan-Harandi
Keyword(s):  
Text Mining ◽  
Comprehensive Plan

Digital Historicism and the Historicity of Digital Texts

2019 ◽  
pp. 66-100
Author(s):  
James E. Dobson
Keyword(s):  
Text Mining ◽  
Computational Methods ◽  
Historical Research ◽  
Affective Content ◽  
Digital Texts ◽  
Sentiment Mining ◽  
Interpretive Framework

This chapter takes up several important theoretical problems and complexities introduced by text mining and datafication to historiography and historical research in order to think about the problems and promises of a digital historicism. The chapter argues for an approach that takes the historicity of the digitized archive seriously without reducing the use of computational methods to either those framed strictly by the terms and language of the present or to a form of rigid historicism that would require enclosing the archive in synchronically constructed interpretive framework. Many of the approaches used within text mining deploy secondary archives—dictionaries, thesauruses, and other forms of human-constructed schemata—that have tended to capture categories used in the present. The chapter concludes by examining the methods and practice of extracting and analyzing emotional or affective content in texts through what is called sentiment mining. Functioning as a case study, sentiment mining demonstrates the need for quantitative and computational humanists to give more attention to the historical dimensions of both text and affect, to both primary and secondary digital sources.

The Effects of the State of Union Address of USA Presidents to Their Performance Ratings

2015 ◽  
Vol 3 (2) ◽  
pp. 1-12
Author(s):  
Carl Lee
Keyword(s):  
Text Mining ◽  
Performance Ratings ◽  
Presidential Approval ◽  
Approval Ratings ◽  
Mining Technique ◽  
State Of The Union ◽  
Speech Patterns ◽  
Modeling Techniques ◽  
Presidential Approval Ratings

In this article, the authors conduct a case study using text mining technique to analyze the patterns of the president's State of the Union Address in USA, and investigate the effects of these speech patterns on their performance rating in the following year. The speeches analyzed include the recent four USA presidents, Bush (1989 – 1992), Clinton (1993 - 2000), G.W. Bush (2001 – 2008), and Obama (2009 – 2011). The patterns found are further integrated and merged with over 4000 surveys on the presidents' performance ratings from 1989 to 2010. Two text mining methodology are applied to study the text patterns. Two predictive modeling techniques are applied to study the effects of these found patterns to their presidential approval ratings. The results indicate that the speech patterns found are highly associated with their approval rates.

scholarly journals Extracting kinetic information from literature with KineticRE

2015 ◽  
Vol 12 (4) ◽  
pp. 56-68
Author(s):  
Ana Alão Freitas ◽  
Hugo Costa ◽  
Isabel Rocha
Keyword(s):  
Text Mining ◽  
Metabolic Networks ◽  
Scientific Literature ◽  
Kluyveromyces Lactis ◽  
Relevant Information ◽  
Text Documents ◽  
Kinetic Information ◽  
Mining Tool ◽  
Text Mining Tool

Summary To better understand the dynamic behavior of metabolic networks in a wide variety of conditions, the field of Systems Biology has increased its interest in the use of kinetic models. The different databases, available these days, do not contain enough data regarding this topic. Given that a significant part of the relevant information for the development of such models is still wide spread in the literature, it becomes essential to develop specific and powerful text mining tools to collect these data. In this context, this work has as main objective the development of a text mining tool to extract, from scientific literature, kinetic parameters, their respective values and their relations with enzymes and metabolites. The approach proposed integrates the development of a novel plug-in over the text mining framework @Note2. In the end, the pipeline developed was validated with a case study on Kluyveromyces lactis, spanning the analysis and results of 20 full text documents.

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