Text Mining to Support Consulting Services for Client Company State Recognition

2020 ◽  
Vol 14 (5) ◽  
pp. 779-790
Author(s):  
Ruriko Watanabe ◽  
Nobutada Fujii ◽  
Daisuke Kokuryo ◽  
Toshiya Kaihara ◽  
Yoichi Abe ◽  
...  
Keyword(s):  
Text Mining ◽  
Support System ◽  
Computer Experiments ◽  
Text Data ◽  
Logistic Regression Models ◽  
Service Companies ◽  
Consulting Services ◽  
Customer Information ◽  
Problem Detection ◽  
Specialized Service

This study was conducted to devise a method for supporting consulting service companies in their response to client demands irrespective of the expertise of consultants. With emphasis on revitalization of small and medium-sized enterprises, the importance of support systems for consulting services to serve them is increasing. Those systems must support solutions to difficulties that must be addressed by enterprises. Consulting companies can respond to widely various management consultations. Nevertheless, because the consultation contents are highly specialized, service proposals and problem detection depend on the experience and intuition of the consultant. Often, stable service cannot be provided. A support system must provide stable services independent of the ability of consultants. In this study, analyzing customer information describing the contents of consultation with client companies is the first step in constructing a support system that can predict future problems. Text data such as a consultant’s visit history, consultation contents by e-mail, and contents of call centers are used for analyses because the contents can explain current problems. They might also indicate future problems. This report describes a method to analyze text data using text mining. The target problem is fraud, which includes uncertainty: cases in which it is not clear whether a fraud problem has occurred with the company. To address uncertainty, a method of using logistic regression models is proposed to represent inferred values as probabilities, rather than as binary discriminated data, because the possibility exists that some misidentified companies might have some difficulty. As described herein, computer experiments are conducted to verify the effectiveness of the proposed method and to compare consultants’ forecasted and achieved results. Results of a verification experiment are presented in the following. First, the proposed method is applicable to problems including uncertainties. Secondly, the possibility exists of discovering companies with a fraud problem of which they are unaware.

A Study on Support Method of Consulting Service Using Text Mining

2018 ◽  
Vol 12 (4) ◽  
pp. 482-491 ◽  
Author(s):  
Ruriko Watanabe ◽  
Nobutada Fujii ◽  
Daisuke Kokuryo ◽  
Toshiya Kaihara ◽  
Yoichi Abe ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Text Mining ◽  
Support System ◽  
Call Center ◽  
Computer Experiments ◽  
Text Data ◽  
Consulting Services ◽  
Customer Information ◽  
Wide Range ◽  
Problem Detection

This study aims to build a support method for consulting service companies allowing them to respond to client demands regardless of the expertise of the consultants. With an emphasis on the revitalization of small and medium-sized enterprises, the importance of support systems for consulting services for small and medium-sized enterprises, which support solving problems that are difficult to deal with by an enterprise, is increasing. Consulting companies can respond to a wide range of management consultations; however, because the contents of a consultation are widely and highly specialized, a service proposal and the problem detection depend on the experience and intuition of the consultant, and thus a stable service may occasionally not be provided. Therefore, a support system for providing stable services independent of the ability of consultants is desired. In this research, as the first step in constructing a support system, an analysis of customer information describing the content of a consultation with the client companies is conducted to predict the occurrence of future problems. Text data such as the consultant’s visitation history, consultation content by e-mail, and call center content are used in the analysis because the contents explain not only the current problems but also possibly contain future problems. This paper describes a method for analyzing the text data by employing text mining. In the proposed method, by combining a correspondence analysis with a DEA (Data Envelopment Analysis) discriminant analysis, words that are strongly related to problem detection are extracted from a large number of words obtained from text data, and variables of the DEA discriminant analysis are reduced and analyzed. The proposed method focuses on a cancellation of contract problems. The cancellation problem does not include uncertainty; it is clearly known whether the contract of the consulting service is being updated or cancelled. In this study, computer experiments were conducted to verify the effectiveness of the proposed method through a comparison with an existing method. The results of the verification experiment are as follows. First, there is a possibility of discovering new factors that cannot be determined from the intuition and experience of the consultant regarding the target problem. Second, through a comparison with the existing method, the effectiveness of the proposed method was confirmed.

scholarly journals Modern types of consulting in the management system of an educational organisation

2021 ◽  
Vol 23 (1) ◽  
pp. 73-101
Author(s):  
I. A. Korshunov ◽  
G. A. Chakhoyan ◽  
A. M. Tyunin ◽  
E. L. Lyakhovetskaya
Keyword(s):  
Open Data ◽  
Practical Significance ◽  
Educational Organizations ◽  
Text Data ◽  
Managerial Practices ◽  
Internal Resources ◽  
External Consultants ◽  
Consulting Services ◽  
Managerial Decisions ◽  
Potential Market

Introduction. In market economy, the internal and external processes of educational organisations are becoming more complex. There is a need to identify managerial practices, which can be delegated to external consultants. The process of defining such practices is not completely studied. The application of expert support from leaders and management teams is often situational.The aim of the present research is to identify the range of managerial problems of educational organisations, which cause the need to resort to external consulting support.Methodology and research methods. The application of the method of cluster analysis of text data of periodicals allowed the authors to investigate a potential market for consulting services, to identify the modern management needs of educational organizations and the experience of their solution by external organisations. The analysis of open data of consulting organisations was carried out to identify real practices.Results. The main management problems of educational organisations and the types of consulting services necessary for their solution are considered and classified. The main approaches to defining the framework of educational consulting are highlighted. The authors have developed the method for monitoring the prospective market for consulting services. It was determined that the growing demand for consulting services and the expansion of the range of these services are associated with the formation of a market environment in the education system and the desire of management of educational organisations to provide a competitive advantage. The cases of Russian and foreign consulting organisations were analysed and grouped in accordance with the proposed typology.Scientific novelty. In academic literature, the study of this issue is mainly limited to defining the boundaries of educational consulting. The authors have made an attempt to identify typical situations, in which managers of educational organisations need to request the services of external experts.Practical significance. The results of the current research can serve as a guideline for the management of educational organisations, when making managerial decisions. It will allow the managers to determine the cases, in which it is possible to turn to external consultants according to the supply conditions on the market, and to identify the issues, regarding which it is desirable to rely solely on internal resources.

Incorporating Text OLAP in Business Intelligence

2011 ◽  
pp. 77-101 ◽  
Author(s):  
Byung-Kwon Park ◽  
Il-Yeol Song
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Business Intelligence ◽  
Multidimensional Analysis ◽  
Web Pages ◽  
Data Types ◽  
Text Documents ◽  
Text Data ◽  
Platform Architecture ◽  
Unstructured Text

As the amount of data grows very fast inside and outside of an enterprise, it is getting important to seamlessly analyze both data types for total business intelligence. The data can be classified into two categories: structured and unstructured. For getting total business intelligence, it is important to seamlessly analyze both of them. Especially, as most of business data are unstructured text documents, including the Web pages in Internet, we need a Text OLAP solution to perform multidimensional analysis of text documents in the same way as structured relational data. We first survey the representative works selected for demonstrating how the technologies of text mining and information retrieval can be applied for multidimensional analysis of text documents, because they are major technologies handling text data. And then, we survey the representative works selected for demonstrating how we can associate and consolidate both unstructured text documents and structured relation data for obtaining total business intelligence. Finally, we present a future business intelligence platform architecture as well as related research topics. We expect the proposed total heterogeneous business intelligence architecture, which integrates information retrieval, text mining, and information extraction technologies all together, including relational OLAP technologies, would make a better platform toward total business intelligence.

scholarly journals User Acceptance of Shuttle Bus Decision Support System Based on Smartphone Probes

SAGE Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 215824402091952
Author(s):  
Melih Engin ◽  
Nihat Kasap ◽  
Berna Tektas Sivrikaya
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Behavioral Intention ◽  
User Acceptance ◽  
Equation Modeling ◽  
Moderator Effect ◽  
Service Companies ◽  
Service Information ◽  
Actual Use

One of the most common procedures in service transport is to adjust the service location information of the passengers or the bus stops. Service companies assign a staff to update service information. These staff prepare lists of passengers and give them to service drivers. In this study, a model is proposed to facilitate the process of updating the service. With the smartphone application, a platform has been prepared in which passengers can automatically update their service information by selecting one of the three closest stops offered to them before the service hour. After development and implementation, its user acceptance is tested and validated via the unified theory of acceptance and use of technology (UTAUT) model and structural equation modeling. The main purpose of this study is to determine the acceptance and use of the mobile passenger application by the passengers. In this context, the moderator effect of the frequency of mobile use of passengers, education levels, genders, and ages on the performance expectancy, effort expectancy, facilitating condition, behavioral intention, and use behavior of passenger mobile applications was examined. As a result of the research, it was found out that the frequency of mobile usage has a moderator effect on both the effect of facilitation conditions on the actual use and the effect of behavioral intention on actual use. And also, it appears that the education level has a moderator effect on the effect of facilitating conditions on the actual use, and gender has a moderator effect on the effect of behavioral intention on the actual use. The proposed decision support system reduces the workload of service companies and facilitates the process, which is normally long and painful. Thus, customer satisfaction also increases. Hence, position and stop information between the service firm, passengers, and the drivers is easily and instantly shared.

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.

scholarly journals A Decision Support System Using Text Mining Based Grey Relational Method for the Evaluation of Written Exams

Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1426
Author(s):  
Mehmet Erkan Yuksel ◽  
Huseyin Fidan
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Text Mining ◽  
Decision Support System ◽  
Support System ◽  
Grey System Theory ◽  
Database Management System ◽  
Data Set ◽  
Relational Analysis ◽  
Grey Relational

Grey relational analysis (GRA) is a part of the Grey system theory (GST). It is appropriate for solving problems with complicated interrelationships between multiple factors/parameters and variables. It solves multiple-criteria decision-making problems by combining the entire range of performance attribute values being considered for every alternative into one single value. Thus, the main problem is reduced to a single-objective decision-making problem. In this study, we developed a decision support system for the evaluation of written exams with the help of GRA using contextual text mining techniques. The answers obtained from the written exam with the participation of 50 students in a computer laboratory and the answer key prepared by the instructor constituted the data set of the study. A symmetrical perspective allows us to perform relational analysis between the students’ answers and the instructor’s answer key in order to contribute to the measurement and evaluation. Text mining methods and GRA were applied to the data set through the decision support system employing the SQL Server database management system, C#, and Java programming languages. According to the results, we demonstrated that the exam papers are successfully ranked and graded based on the word similarities in the answer key.

Dual Scaling in Data Mining from Text Databases

2006 ◽  
Vol 10 (4) ◽  
pp. 451-457 ◽  
Author(s):  
Junzo Watada ◽  
◽  
Keisuke Aoki ◽  
Masahiro Kawano ◽  
Muhammad Suzuri Hitam ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Text Mining ◽  
Kansei Engineering ◽  
Semantic Meaning ◽  
Dual Scaling ◽  
Text Documents ◽  
Text Data ◽  
Text Document ◽  
Text Information ◽  
Quantification Model

The availability of multimedia text document information has disseminated text mining among researchers. Text documents, integrate numerical and linguistic data, making text mining interesting and challenging. We propose text mining based on a fuzzy quantification model and fuzzy thesaurus. In text mining, we focus on: 1) Sentences included in Japanese text that are broken down into words. 2) Fuzzy thesaurus for finding words matching keywords in text. 3) Fuzzy multivariate analysis to analyze semantic meaning in predefined case studies. We use a fuzzy thesaurus to translate words using Chinese and Japanese characters into keywords. This speeds up processing without requiring a dictionary to separate words. Fuzzy multivariate analysis is used to analyze such processed data and to extract latent mutual related structures in text data, i.e., to extract otherwise obscured knowledge. We apply dual scaling to mining library and Web page text information, and propose integrating the result in Kansei engineering for possible application in sales, marketing, and production.

The Method to Analyze Freely Described Data from Questionnaires

2009 ◽  
Vol 13 (3) ◽  
pp. 268-274 ◽  
Author(s):  
Masaomi Kimura ◽  
Keyword(s):  
Text Mining ◽  
Call Center ◽  
Clustering Algorithms ◽  
Web Pages ◽  
Research Papers ◽  
Text Data ◽  
Textual Data ◽  
The Way ◽  
Newspaper Articles

Text mining has been growing; mainly due to the need to extract useful information from vast amounts of textual data. Our target here is text data, a collection of freely described data from questionnaires. Unlike research papers, newspaper articles, call-center logs and web pages, which are usually the targets of text mining analysis, the freely described data contained in the questionnaire responses have specific characteristics, including a small number of short sentences forming individual pieces of data, while the wide variety of content precludes the applications of clustering algorithms used to classify the same. In this paper, we suggest the way to extract the opinions which are delivered by multiple respondents, based on the modification relationships included in each sentence in the freely described data. Certain applications of our method are also presented after the introduction of our approach.

Sign in / Sign up

Export Citation Format

Share Document