Topic Analysis of COVID-19 and Childcare Center Closures Using Text Mining Technique

2021 ◽  
Vol 21 (1) ◽  
pp. 67-80
Author(s):  
Mi Young Sung ◽  
Byungtae Seo ◽  
Min Suk Choi ◽  
Mi Na Noh
Keyword(s):  
Text Mining ◽  
Topic Analysis ◽  
Mining Technique ◽  
Childcare Center

Evaluation of Universities' Integrated Reports Using Text Mining Technique

2021 ◽  
Author(s):  
Takumi Miura ◽  
Takumi Furukawa ◽  
Junko Harada ◽  
Yudai Hirano ◽  
Takako Hashimoto
Keyword(s):  
Text Mining ◽  
Mining Technique

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.

Looking beyond the stars: A description of text mining technique to extract latent dimensions from online product reviews

2019 ◽  
Vol 62 (2) ◽  
pp. 195-215
Author(s):  
Frederik Situmeang ◽  
Nelleke de Boer ◽  
Austin Zhang
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Research Methodology ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Product Reviews ◽  
Marketing Literature ◽  
Mining Technique ◽  
Online Product Reviews ◽  
The Relationship

The purpose of this study is to contribute to the marketing literature and practice by describing a research methodology to identify latent dimensions of customer satisfaction in product reviews, and examining the relationship between these attributes and customer satisfaction. Previous research in product reviews has largely relied only on quantitative ratings, either stars or review score. Advanced techniques for text mining provide the opportunity to extract meaning from customer online reviews. By analyzing 51,110 online reviews for 1,610 restaurants via latent Dirichlet allocation, this study uncovers 30 latent dimensions that are determinants of customer satisfaction. Furthermore, this study developed measurements of sentiment and innovativeness as moderators of the effect of these latent attributes to satisfaction.

Knowledge discovery out of text data: a systematic review via text mining

2018 ◽  
Vol 22 (7) ◽  
pp. 1471-1488 ◽  
Author(s):  
Antonio Usai ◽  
Marco Pironti ◽  
Monika Mital ◽  
Chiraz Aouina Mejri
Keyword(s):  
Systematic Review ◽  
Text Mining ◽  
Knowledge Discovery ◽  
Research Collaboration ◽  
Collaborative Writing ◽  
Web Based ◽  
Diverse Range ◽  
Content Type ◽  
Mining Technique ◽  
Database Technology

Purpose The aim of this work is to increase awareness of the potential of the technique of text mining to discover knowledge and further promote research collaboration between knowledge management and the information technology communities. Since its emergence, text mining has involved multidisciplinary studies, focused primarily on database technology, Web-based collaborative writing, text analysis, machine learning and knowledge discovery. However, owing to the large amount of research in this field, it is becoming increasingly difficult to identify existing studies and therefore suggest new topics. Design/methodology/approach This article offers a systematic review of 85 academic outputs (articles and books) focused on knowledge discovery derived from the text mining technique. The systematic review is conducted by applying “text mining at the term level, in which knowledge discovery takes place on a more focused collection of words and phrases that are extracted from and label each document” (Feldman et al., 1998, p. 1). Findings The results revealed that the keywords extracted to be associated with the main labels, id est, knowledge discovery and text mining, can be categorized in two periods: from 1998 to 2009, the term knowledge and text were always used. From 2010 to 2017 in addition to these terms, sentiment analysis, review manipulation, microblogging data and knowledgeable users were the other terms frequently used. Besides this, it is possible to notice the technical, engineering nature of each term present in the first decade. Whereas, a diverse range of fields such as business, marketing and finance emerged from 2010 to 2017 owing to a greater interest in the online environment. Originality/value This is a first comprehensive systematic review on knowledge discovery and text mining through the use of a text mining technique at term level, which offers to reduce redundant research and to avoid the possibility of missing relevant publications.

Automatic lyrics classification system using text mining technique

2018 ◽  
Author(s):  
Chatklaw Jareanpon ◽  
Waranyoo Kiatjindarat ◽  
Thanawat Polhome ◽  
Kittiya Khongkraphan
Keyword(s):  
Text Mining ◽  
Classification System ◽  
Mining Technique
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