Article

Mining from open answers in questionnaire data

Authors:

Kenji YamanishiAuthors Info & Claims

KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 443 - 449

https://doi.org/10.1145/502512.502579

Published: 26 August 2001 Publication History

Abstract

Surveys are an important part of marketing and customer relationship management, and open answers (i.e., answers to open questions) in particular may contain valuable information and provide an important basis for making business decisions. We have developed a text mining system that provides a new way for analyzing open answers in questionnaire data. The product is able to perform the following two functions: (A) accurate extraction of characteristics for individual analysis targets, (B) accurate extraction of the relationships among characteristics of analysis targets. In this paper, we describe the working of our text mining system. It employs two statistical learning techniques: rule analysis and Correspondence Analysis for performing the two functions. Our text mining system has already been put into use by a number of large corporations in Japan in the performance of text mining on various types of survey data, including open answers about brand images, open answers about company images, complaints about products, comments written on home pages, business reports, and help desk records. In this it has been found to be useful in forming a basis for effective business decisions.

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Cited By

Dalla Costa ETranquillo VDai FMinero MBattini MMattiello SBarbieri SFerrante VFerrari LZanella ACanali E(2019)Text Mining Analysis to Evaluate Stakeholders’ Perception Regarding Welfare of Equines, Small Ruminants, and TurkeysAnimals10.3390/ani90502259:5(225)Online publication date: 8-May-2019
https://doi.org/10.3390/ani9050225
Wijngaards IBurger Mvan Exel J(2019)The promise of open survey questions—The validation of text-based job satisfaction measuresPLOS ONE10.1371/journal.pone.022640814:12(e0226408)Online publication date: 26-Dec-2019
https://doi.org/10.1371/journal.pone.0226408
Singh SSachan M(2019)SentiVerb system: classification of social media text using sentiment analysisMultimedia Tools and Applications10.1007/s11042-019-07995-2Online publication date: 30-Jul-2019
https://doi.org/10.1007/s11042-019-07995-2
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Index Terms

Mining from open answers in questionnaire data
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Triggers and rules
  2. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining

August 2001

493 pages

ISBN:158113391X

DOI:10.1145/502512

Conference Chair:
Doheon Lee
Chonnam National University, Korea
,
General Chair:
Mario Schkolnick
SGI
,
Program Chairs:
Foster Provost
New York University
,
Ramakrishnan Srikant
IBM Almaden Research Center

Copyright © 2001 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGMOD: ACM Special Interest Group on Management of Data
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
AAAI: American Association for Artificial Intelligence

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 August 2001

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KDD01

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KDD01: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 26 - 29, 2001

California, San Francisco

Acceptance Rates

KDD '01 Paper Acceptance Rate 31 of 237 submissions, 13%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Dalla Costa ETranquillo VDai FMinero MBattini MMattiello SBarbieri SFerrante VFerrari LZanella ACanali E(2019)Text Mining Analysis to Evaluate Stakeholders’ Perception Regarding Welfare of Equines, Small Ruminants, and TurkeysAnimals10.3390/ani90502259:5(225)Online publication date: 8-May-2019
https://doi.org/10.3390/ani9050225
Wijngaards IBurger Mvan Exel J(2019)The promise of open survey questions—The validation of text-based job satisfaction measuresPLOS ONE10.1371/journal.pone.022640814:12(e0226408)Online publication date: 26-Dec-2019
https://doi.org/10.1371/journal.pone.0226408
Singh SSachan M(2019)SentiVerb system: classification of social media text using sentiment analysisMultimedia Tools and Applications10.1007/s11042-019-07995-2Online publication date: 30-Jul-2019
https://doi.org/10.1007/s11042-019-07995-2
Li GJin XWang ZChen XWu X(2018)Expert Recommendation Based on Collaborative Filtering in Subject ResearchProceedings of the 1st International Conference on Information Science and Systems10.1145/3209914.3209939(291-298)Online publication date: 27-Apr-2018
https://dl.acm.org/doi/10.1145/3209914.3209939
Mazzoleni MMaroni GPrevidi F(2017)Unsupervised learning of fundamental emotional states via word embeddings2017 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2017.8280819(1-6)Online publication date: Nov-2017
https://doi.org/10.1109/SSCI.2017.8280819
Aggarwal ASingh A(2017)Geo-localized public perception visualization using GLOPP for social media2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)10.1109/IEMCON.2017.8117192(439-445)Online publication date: Oct-2017
https://doi.org/10.1109/IEMCON.2017.8117192
Singh BKushwaha NVyas O(2016)An interpretation of sentiment analysis for enrichment of Business Intelligence2016 IEEE Region 10 Conference (TENCON)10.1109/TENCON.2016.7847950(18-23)Online publication date: Nov-2016
https://doi.org/10.1109/TENCON.2016.7847950
Patil SRavindran B(2015)Active Learning Based Weak Supervision for Textual Survey Response ClassificationComputational Linguistics and Intelligent Text Processing10.1007/978-3-319-18117-2_23(309-320)Online publication date: 2015
https://doi.org/10.1007/978-3-319-18117-2_23
(2014)Simple correspondence analysisCorrespondence Analysis10.1002/9781118762875.ch04(120-176)Online publication date: 29-Aug-2014
https://doi.org/10.1002/9781118762875.ch04
Sterbini ATemperini M(2012)Supporting Assessment of Open Answers in a Didactic SettingProceedings of the 2012 IEEE 12th International Conference on Advanced Learning Technologies10.1109/ICALT.2012.149(678-679)Online publication date: 4-Jul-2012
https://dl.acm.org/doi/10.1109/ICALT.2012.149
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