Compilation of tweets sentiment into SERVQUAL for tracking social perception on public service

Hong Joo Lee; Minsik Lee; Habin Lee

doi:10.1145/3154943.3154952

Compilation of tweets sentiment into SERVQUAL for tracking social perception on public service

Hong Joo Lee, Minsik Lee, Habin Lee

Business Administration

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Though, there are opportunities and demands of utilizing social media for supporting public policy making processes, a way to compilate sentiments on social media into service quality measurements is yet to be proposed in the literature. This paper suggests a systematic method to transform sentiments of tweets into SERVQUAL constructs for tracking of perceived service quality of NHS in the UK. In this study, we propose a methodology of identifying more reliable topic sets by repeating LDA and clustering topic sets, and determine the meanings of topics guided by an existing theory in service quality. To show the applicability of our method, we select healthcare as our target area and pick NHS of U.K for measuring service quality of public policy. We collected tweets about NHS for about 4 years and applied the suggested methodology.

Original language	English
Title of host publication	Proceedings of the International Conference on Electronic Commerce, ICEC 2017
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450353120
DOIs	https://doi.org/10.1145/3154943.3154952
State	Published - 17 Aug 2017
Event	2017 International Conference on Electronic Commerce, ICEC 2017 - Pangyo, Seongnam, Korea, Republic of Duration: 17 Aug 2017 → 18 Aug 2017

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2017 International Conference on Electronic Commerce, ICEC 2017
Country/Territory	Korea, Republic of
City	Pangyo, Seongnam
Period	17/08/17 → 18/08/17

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government(NRF-2016S1A2A2912265).

Publisher Copyright:
© Copyright 2017 ACM

Keywords

Healthcare
NHS
Sentiment analysis
SERVQUAL
Social perceptions
Topic modeling

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/3154943.3154952

Cite this

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title = "Compilation of tweets sentiment into SERVQUAL for tracking social perception on public service",

abstract = "Though, there are opportunities and demands of utilizing social media for supporting public policy making processes, a way to compilate sentiments on social media into service quality measurements is yet to be proposed in the literature. This paper suggests a systematic method to transform sentiments of tweets into SERVQUAL constructs for tracking of perceived service quality of NHS in the UK. In this study, we propose a methodology of identifying more reliable topic sets by repeating LDA and clustering topic sets, and determine the meanings of topics guided by an existing theory in service quality. To show the applicability of our method, we select healthcare as our target area and pick NHS of U.K for measuring service quality of public policy. We collected tweets about NHS for about 4 years and applied the suggested methodology.",

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author = "Lee, {Hong Joo} and Minsik Lee and Habin Lee",

note = "Funding Information: This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government(NRF-2016S1A2A2912265). Publisher Copyright: {\textcopyright} Copyright 2017 ACM; 2017 International Conference on Electronic Commerce, ICEC 2017 ; Conference date: 17-08-2017 Through 18-08-2017",

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doi = "10.1145/3154943.3154952",

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series = "ACM International Conference Proceeding Series",

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Lee, HJ, Lee, M & Lee, H 2017, Compilation of tweets sentiment into SERVQUAL for tracking social perception on public service. in Proceedings of the International Conference on Electronic Commerce, ICEC 2017. ACM International Conference Proceeding Series, Association for Computing Machinery, 2017 International Conference on Electronic Commerce, ICEC 2017, Pangyo, Seongnam, Korea, Republic of, 17/08/17. https://doi.org/10.1145/3154943.3154952

Compilation of tweets sentiment into SERVQUAL for tracking social perception on public service. / Lee, Hong Joo; Lee, Minsik; Lee, Habin.
Proceedings of the International Conference on Electronic Commerce, ICEC 2017. Association for Computing Machinery, 2017. (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Compilation of tweets sentiment into SERVQUAL for tracking social perception on public service

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N2 - Though, there are opportunities and demands of utilizing social media for supporting public policy making processes, a way to compilate sentiments on social media into service quality measurements is yet to be proposed in the literature. This paper suggests a systematic method to transform sentiments of tweets into SERVQUAL constructs for tracking of perceived service quality of NHS in the UK. In this study, we propose a methodology of identifying more reliable topic sets by repeating LDA and clustering topic sets, and determine the meanings of topics guided by an existing theory in service quality. To show the applicability of our method, we select healthcare as our target area and pick NHS of U.K for measuring service quality of public policy. We collected tweets about NHS for about 4 years and applied the suggested methodology.

AB - Though, there are opportunities and demands of utilizing social media for supporting public policy making processes, a way to compilate sentiments on social media into service quality measurements is yet to be proposed in the literature. This paper suggests a systematic method to transform sentiments of tweets into SERVQUAL constructs for tracking of perceived service quality of NHS in the UK. In this study, we propose a methodology of identifying more reliable topic sets by repeating LDA and clustering topic sets, and determine the meanings of topics guided by an existing theory in service quality. To show the applicability of our method, we select healthcare as our target area and pick NHS of U.K for measuring service quality of public policy. We collected tweets about NHS for about 4 years and applied the suggested methodology.

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M3 - Conference contribution

AN - SCOPUS:85061242257

T3 - ACM International Conference Proceeding Series

BT - Proceedings of the International Conference on Electronic Commerce, ICEC 2017

PB - Association for Computing Machinery

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Y2 - 17 August 2017 through 18 August 2017

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Compilation of tweets sentiment into SERVQUAL for tracking social perception on public service

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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Cite this