Summarization and Visualization of Characteristics of a Workshop Discussion using Text Mining Method

2011 ◽  
Vol 46 (3) ◽  
pp. 1039-1044
Author(s):  
Kuniaki Sasaki ◽  
Koh-ichi Maruishi
Keyword(s):  
Text Mining ◽  
Mining Method ◽  
Workshop Discussion

Complaint management model of manufacturing products using text mining and potential failure identification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ririn Diar Astanti ◽  
Ivana Carissa Sutanto ◽  
The Jin Ai
Keyword(s):  
Quality Management ◽  
Text Mining ◽  
Failure Mode ◽  
Management System ◽  
Main Part ◽  
Mining Method ◽  
Content Type ◽  
Complaint Management ◽  
Potential Failure ◽  
Failure Identification

PurposeThis paper aims to propose a framework on complaint management system for quality management by applying the text mining method and potential failure identification that can support organization learning (OL). Customer complaints in the form of email text is the input of the framework, while the most frequent complaints are visualized using a Pareto diagram. The company can learn from this Pareto diagram and take action to improve their process.Design/methodology/approachThe first main part of the framework is creating a defect database from potential failure identification, which is the initial part of the failure mode and effect analysis technique. The second main part is the text mining of customer email complaints. The last part of the framework is matching the result of text mining with the defect database and presenting in the form of a Pareto diagram. After the framework is proposed, a case study is conducted to illustrate the applicability of the proposed method.FindingsBy using the defect database, the framework can interpret the customer email complaints into the list of most defect complained by customer using a Pareto diagram. The results of the Pareto diagram, based on the results of text mining of consumer complaints via email, can be used by a company to learn from complaint and to analyze the potential failure mode. This analysis helps company to take anticipatory action for avoiding potential failure mode happening in the future.Originality/valueThe framework on complaint management system for quality management by applying the text mining method and potential failure identification is proposed for the first time in this paper.

Determination of Guest Satisfaction by Text Mining

2022 ◽  
pp. 247-269
Author(s):  
Ozan Çatir
Keyword(s):  
Text Mining ◽  
Association Analysis ◽  
Hotel Management ◽  
Mining Method ◽  
Online Comments ◽  
Guest Satisfaction

The satisfaction of guests is of paramount importance to ensure the continuity and profitability of hotels. This study aims to determine guests' satisfaction with hotels by analyzing the online comments of guests. The text mining method has been utilized in this study. 58,193 Turkish comments about 5-star hotels in Turkey have been examined. These comments have been subjected to frequency and association analysis by models with Rapid Miner program. It may be stated that the guests are satisfied with 5-star hotel management in Turkey, and they are also satisfied with hotels in general and the services provided by hotels.

scholarly journals Use of a text mining method for classifying citizen report data and analyzing the occurrence trend of local problems

2019 ◽  
Vol 8 (2) ◽  
pp. 1
Author(s):  
Eiji Kano ◽  
Kazuhiko Tsuda
Keyword(s):  
Text Mining ◽  
Traffic Safety ◽  
Living Environment ◽  
Mining Method ◽  
Local Problems ◽  
Problem Detection ◽  
Report System ◽  
Report Data ◽  
Maintenance And Inspection ◽  
The Relationship

An important task of any municipality is the maintenance and improvement of the street-related living environment and traffic safety for citizens.  For this, their department of street maintenance is expected to efficiently perform the maintenance and inspection of streets according to priority with limited human and budgetary resources.  Recently, municipalities in various countries are adopting “the citizen report system,” which is a system of reporting problems of streets, such as damaged streets, by citizens to their municipality, for citizens to perform part of street maintenance and inspection.  It is possible that the data obtained by municipalities through the citizen report system can be utilized not only for early problem detection but also for prioritizing administrative measures by using it for analyzing the occurrence trend of problems.  Problems reported by citizens, however, are classified by different methods from municipality to municipality, and thus the collection and comparative analysis of such data across municipalities is difficult.  This study presents a method of commonly classifying such data, regardless of different classification standards, by analyzing the contents of citizen reports by using text mining.  We then analyze the relationship between the trend of citizen reports and the occurrence trend of problems concerning the living environment and traffic safety, using the citizen report data of three large municipalities classified by this method, and infer the occurrence trend of problems.  This study has confirmed that citizen report data possibly contributes to municipalities’ prioritization of the maintenance and improvement of the living environment and traffic safety.

Japanese anti- versus pro-influenza vaccination websites: a text-mining analysis

2018 ◽  
Vol 34 (3) ◽  
pp. 552-566 ◽  
Author(s):  
Tsuyoshi Okuhara ◽  
Hirono Ishikawa ◽  
Masafumi Okada ◽  
Mio Kato ◽  
Takahiro Kiuchi
Keyword(s):  
Side Effects ◽  
Text Mining ◽  
Influenza Vaccination ◽  
Health Professionals ◽  
Vaccine Coverage ◽  
The Other ◽  
Efficacy Rate ◽  
Mining Method ◽  
Chi Squared ◽  
Online Searches

Summary Anti-vaccination sentiment exists worldwide and Japan is no exception. Health professionals publish pro-influenza vaccination messages online to encourage proactive seeking of influenza vaccination. However, influenza vaccine coverage among the Japanese population is less than optimal. The contents of pro- and anti-influenza vaccination websites may contribute to readers’ acceptance of one or the other position. We aimed to use a text-mining method to examine frequently appearing content on websites for and against influenza vaccination. We conducted online searches in January 2017 using two major Japanese search engines (Google Japan and Yahoo! Japan). Targeted websites were classified as ‘pro’, ‘anti’ or ‘neutral’ depending on their claims, with author(s) classified as ‘health professionals’, ‘mass media’ or ‘laypersons’. Text-mining analysis was conducted, and statistical analysis was performed using a chi-squared test. Of the 334 websites analyzed, 13 content topics were identified. The three most frequently appearing content topics on pro-vaccination websites were vaccination effect for preventing serious cases of influenza, side effects of vaccination, and efficacy rate of vaccination. The three most frequent topics on anti-vaccination websites were ineffectiveness of influenza vaccination, toxicity of vaccination, and side effects of vaccination. The main disseminators of each topic, by author classification, were also revealed. We discuss possible tactics of online influenza vaccination promotion to counter anti-vaccination websites.

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