Topic Analysis in the Study of Financial Consumer Education

2021 ◽  
Vol 21 (19) ◽  
pp. 375-384
Author(s):  
Eun Jeung Kim
Keyword(s):  
Consumer Education ◽  
Topic Analysis

Research Trends of Consumer Education Using Topic Modeling

2020 ◽  
Vol 16 (2) ◽  
pp. 83-115
Author(s):  
Mira Kim ◽  
◽  
Hye Sun Hwang ◽  
Xu Li
Keyword(s):  
Topic Modeling ◽  
Research Trends ◽  
Consumer Education

Home Buyers’ and Vendors’ Behaviours: Analysis of Judicial Decisions

2017 ◽  
Vol 2 (6) ◽  
pp. 229
Author(s):  
Nuraisyah Chua Abdullah ◽  
Ramzyzan Ramly ◽  
Muhammad Izwan Ikhsan
Keyword(s):  
United States ◽  
The United States ◽  
Consumer Education ◽  
Judicial Decisions

This paper examines the behaviour of vendors and purchasers indirectly through the judicial decisions in Malaysia, Australia, and the United States. The decided cases illustrate that buyers are still indolent in their duty to conduct pre-purchase inspections, some vendors were seen to have actively concealed defects in the property and fraudulently misrepresented the conditions of the properties. This paper suggests consumer education for both the vendors and purchasers and the extension of the jurisdiction of either the Tribunal for Homebuyers Claims or the Tribunal for Consumer Claims to include matters regarding the dispute as to the condition of the property.

A bootstrapping approach for robust topic analysis

2002 ◽  
Vol 8 (2-3) ◽  
pp. 209-233 ◽  
Author(s):  
OLIVIER FERRET ◽  
BRIGITTE GRAU
Keyword(s):  
Information Extraction ◽  
Large Scale ◽  
Text Summarization ◽  
Great Precision ◽  
Topic Analysis ◽  
Structured Knowledge

Topic analysis is important for many applications dealing with texts, such as text summarization or information extraction. However, it can be done with great precision only if it relies on structured knowledge, which is difficult to produce on a large scale. In this paper, we propose using bootstrapping to solve this problem: a first topic analysis based on a weakly structured source of knowledge, a collocation network, is used for learning explicit topic representations that then support a more precise and reliable topic analysis.

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