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

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

Publisher Copyright:
© Copyright 2017 ACM

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Healthcare
NHS
SERVQUAL
Sentiment analysis
Social perceptions
Topic modeling

Access to Document

10.1145/3154943.3154952

Cite this

@inproceedings{adc6c8c7f3fd449999c2d9edae6e71a2,

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.",

keywords = "Healthcare, NHS, SERVQUAL, Sentiment analysis, Social perceptions, Topic modeling",

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

year = "2017",

month = aug,

day = "17",

doi = "10.1145/3154943.3154952",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

booktitle = "Proceedings of the International Conference on Electronic Commerce, ICEC 2017",

}

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

TY - GEN

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

AU - Lee, Hong Joo

AU - Lee, Minsik

AU - Lee, Habin

PY - 2017/8/17

Y1 - 2017/8/17

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.

KW - Healthcare

KW - NHS

KW - SERVQUAL

KW - Sentiment analysis

KW - Social perceptions

KW - Topic modeling

UR - https://www.scopus.com/pages/publications/85061242257

U2 - 10.1145/3154943.3154952

DO - 10.1145/3154943.3154952

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

T2 - 2017 International Conference on Electronic Commerce, ICEC 2017

Y2 - 17 August 2017 through 18 August 2017

ER -

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

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

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Cite this