A Study on the Change of Perception of U.S. Forces in Pyeongtaek through Social Network Text Analysis

2021 ◽  
Vol 33 (2) ◽  
pp. 257-281
Author(s):  
Su-woo Kim ◽  
Sung-wook Nam
Keyword(s):  
Social Network ◽  
Text Analysis ◽  
Network Text Analysis

scholarly journals Knowledge Graphs and Network Text Analysis

2003 ◽  
Vol 42 (1) ◽  
pp. 91-106 ◽  
Author(s):  
Roel Popping
Keyword(s):  
Present State ◽  
Text Analysis ◽  
Scientific Theory ◽  
Semantic Network ◽  
Labour Markets ◽  
Knowledge Graph ◽  
Causal Relations ◽  
Network Text Analysis ◽  
Knowledge Graphs

A knowledge graph is a kind of semantic network representing some scientific theory. The article describes the present state of this field and addresses a number of problems that have not yet been solved. These problems are implicit relations, strength of (causal) relations, and exclusiveness. Concepts might be too broad or complex to be used properly, so directions for solving these problems are explored. The solutions are applied to a knowledge graph in the field of labour markets.

scholarly journals AI Model for Predicting Legal Judgments to Improve Accuracy and Explainability of Online Privacy Invasion Cases

2021 ◽  
Vol 11 (23) ◽  
pp. 11080
Author(s):  
Minjung Park ◽  
Sangmi Chai
Keyword(s):  
Text Analysis ◽  
Technological Development ◽  
Online Privacy ◽  
Study Objective ◽  
Societal Factors ◽  
Network Text Analysis ◽  
Improve Accuracy ◽  
Legal Judgments ◽  
The Relationship ◽  
High Possibility

Since there are growing concerns regarding online privacy, firms may have the risk of being involved in various privacy infringement cases resulting in legal causations. If firms are aware of consequences from possible cases of invasion of online privacy, they can more actively prevent future online privacy infringements. Thus, this study attempts to predict the probability of judgment types caused by various invasions within US judicial cases that are related to online privacy invasions. Since legal judgment results are significantly influenced by societal factors and technological development, this study tries to identify a model that can accurately predict legal judgment with explainability. To archive the study objective, it compares the prediction performance by applying five types of classification algorithms (LDA, NNET, CART, SVM, and random forest) of machine learning. We also examined the relationship between privacy infringement factors and adjudications by applying network text analysis. The results indicate that firms could have a high possibility of both civil and criminal law responsibilities if they distributed malware or spyware, intentionally or non-intentionally, to collect unauthorized data. It addresses the needs of reflecting both quantitative and qualitative approach for establishing automatic legal systems for improving its accuracy based on the socio-technical perspective.

Sign in / Sign up

Export Citation Format

Share Document