Customer Complaints Clusterization of Government Drinking Water Company on Social Media Twitter using Text Mining

Ajeng Dewinta; Mohammad Isa Irawan

doi:10.1109/EIConCIT50028.2021.9431931

Customer Complaints Clusterization of Government Drinking Water Company on Social Media Twitter using Text Mining

Ajeng Dewinta, Mohammad Isa Irawan

Laboratory of Machine Learning and Big Data

Institut Teknologi Sepuluh Nopember

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

4 Citations (Scopus)

Abstract

Social media is considered one of the most effective platforms to communicate between companies and customers. Frequently, the customer of a product or service sends complaints via social media. Customers' complaint data serve as a good suggestion for companies and organizations to improve their products and services. With the increasing number of customer complaints that have entered through social media accounts, government-owned drinking water companies need a more efficient way to extract information from complaint data. In this research, text mining is used to extract information about customer complaints against drinking water companies from social media Twitter. Latent Dirichlet Allocation (LDA) and self-organizing maps (SOM) approach is applied to model complaint topics and find out which are most frequently complained the test results indicate grouping the data into five classes is the most appropriate model. Pipes leakage are the most frequently reported topics, 27.8% of total datasets.

Original language	English
Title of host publication	3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021
Editors	Rayner Alfred, Haviluddin Haviluddin, Aji Prasetya Wibawa, Joan Santoso, Fachrul Kurniawan, Hartarto Junaedi, Purnawansyah Purnawansyah, Endang Setyati, Herman Thuan To Saurik, Esther Irawati Setiawan, Eka Rahayu Setyaningsih, Edwin Pramana, Yosi Kristian, Kelvin Kelvin, Devi Dwi Purwanto, Eunike Kardinata, Prananda Anugrah
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	338-342
Number of pages	5
ISBN (Electronic)	9781665405140
DOIs	https://doi.org/10.1109/EIConCIT50028.2021.9431931
Publication status	Published - 9 Apr 2021
Event	3rd East Indonesia Conference on Computer and Information Technology, EIConCIT 2021 - Virtual, Surabaya, Indonesia Duration: 9 Apr 2021 → 11 Apr 2021

Publication series

Name	3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021

Conference

Conference	3rd East Indonesia Conference on Computer and Information Technology, EIConCIT 2021
Country/Territory	Indonesia
City	Virtual, Surabaya
Period	9/04/21 → 11/04/21

Keywords

LDA
SOM
clustering
social media
text mining

Access to Document

10.1109/EIConCIT50028.2021.9431931

Cite this

Dewinta, A., & Irawan, M. I. (2021). Customer Complaints Clusterization of Government Drinking Water Company on Social Media Twitter using Text Mining. In R. Alfred, H. Haviluddin, A. P. Wibawa, J. Santoso, F. Kurniawan, H. Junaedi, P. Purnawansyah, E. Setyati, H. T. T. Saurik, E. I. Setiawan, E. R. Setyaningsih, E. Pramana, Y. Kristian, K. Kelvin, D. D. Purwanto, E. Kardinata, & P. Anugrah (Eds.), 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021 (pp. 338-342). Article 9431931 (3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EIConCIT50028.2021.9431931

Dewinta, Ajeng ; Irawan, Mohammad Isa. / Customer Complaints Clusterization of Government Drinking Water Company on Social Media Twitter using Text Mining. 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021. editor / Rayner Alfred ; Haviluddin Haviluddin ; Aji Prasetya Wibawa ; Joan Santoso ; Fachrul Kurniawan ; Hartarto Junaedi ; Purnawansyah Purnawansyah ; Endang Setyati ; Herman Thuan To Saurik ; Esther Irawati Setiawan ; Eka Rahayu Setyaningsih ; Edwin Pramana ; Yosi Kristian ; Kelvin Kelvin ; Devi Dwi Purwanto ; Eunike Kardinata ; Prananda Anugrah. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 338-342 (3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021).

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title = "Customer Complaints Clusterization of Government Drinking Water Company on Social Media Twitter using Text Mining",

abstract = "Social media is considered one of the most effective platforms to communicate between companies and customers. Frequently, the customer of a product or service sends complaints via social media. Customers' complaint data serve as a good suggestion for companies and organizations to improve their products and services. With the increasing number of customer complaints that have entered through social media accounts, government-owned drinking water companies need a more efficient way to extract information from complaint data. In this research, text mining is used to extract information about customer complaints against drinking water companies from social media Twitter. Latent Dirichlet Allocation (LDA) and self-organizing maps (SOM) approach is applied to model complaint topics and find out which are most frequently complained the test results indicate grouping the data into five classes is the most appropriate model. Pipes leakage are the most frequently reported topics, 27.8% of total datasets.",

keywords = "LDA, SOM, clustering, social media, text mining",

author = "Ajeng Dewinta and Irawan, {Mohammad Isa}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 3rd East Indonesia Conference on Computer and Information Technology, EIConCIT 2021 ; Conference date: 09-04-2021 Through 11-04-2021",

year = "2021",

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doi = "10.1109/EIConCIT50028.2021.9431931",

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series = "3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021",

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editor = "Rayner Alfred and Haviluddin Haviluddin and Wibawa, {Aji Prasetya} and Joan Santoso and Fachrul Kurniawan and Hartarto Junaedi and Purnawansyah Purnawansyah and Endang Setyati and Saurik, {Herman Thuan To} and Setiawan, {Esther Irawati} and Setyaningsih, {Eka Rahayu} and Edwin Pramana and Yosi Kristian and Kelvin Kelvin and Purwanto, {Devi Dwi} and Eunike Kardinata and Prananda Anugrah",

booktitle = "3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021",

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Dewinta, A & Irawan, MI 2021, Customer Complaints Clusterization of Government Drinking Water Company on Social Media Twitter using Text Mining. in R Alfred, H Haviluddin, AP Wibawa, J Santoso, F Kurniawan, H Junaedi, P Purnawansyah, E Setyati, HTT Saurik, EI Setiawan, ER Setyaningsih, E Pramana, Y Kristian, K Kelvin, DD Purwanto, E Kardinata & P Anugrah (eds), 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021., 9431931, 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021, Institute of Electrical and Electronics Engineers Inc., pp. 338-342, 3rd East Indonesia Conference on Computer and Information Technology, EIConCIT 2021, Virtual, Surabaya, Indonesia, 9/04/21. https://doi.org/10.1109/EIConCIT50028.2021.9431931

Customer Complaints Clusterization of Government Drinking Water Company on Social Media Twitter using Text Mining. / Dewinta, Ajeng; Irawan, Mohammad Isa.
3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021. ed. / Rayner Alfred; Haviluddin Haviluddin; Aji Prasetya Wibawa; Joan Santoso; Fachrul Kurniawan; Hartarto Junaedi; Purnawansyah Purnawansyah; Endang Setyati; Herman Thuan To Saurik; Esther Irawati Setiawan; Eka Rahayu Setyaningsih; Edwin Pramana; Yosi Kristian; Kelvin Kelvin; Devi Dwi Purwanto; Eunike Kardinata; Prananda Anugrah. Institute of Electrical and Electronics Engineers Inc., 2021. p. 338-342 9431931 (3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021).

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

TY - GEN

T1 - Customer Complaints Clusterization of Government Drinking Water Company on Social Media Twitter using Text Mining

AU - Dewinta, Ajeng

AU - Irawan, Mohammad Isa

PY - 2021/4/9

Y1 - 2021/4/9

N2 - Social media is considered one of the most effective platforms to communicate between companies and customers. Frequently, the customer of a product or service sends complaints via social media. Customers' complaint data serve as a good suggestion for companies and organizations to improve their products and services. With the increasing number of customer complaints that have entered through social media accounts, government-owned drinking water companies need a more efficient way to extract information from complaint data. In this research, text mining is used to extract information about customer complaints against drinking water companies from social media Twitter. Latent Dirichlet Allocation (LDA) and self-organizing maps (SOM) approach is applied to model complaint topics and find out which are most frequently complained the test results indicate grouping the data into five classes is the most appropriate model. Pipes leakage are the most frequently reported topics, 27.8% of total datasets.

AB - Social media is considered one of the most effective platforms to communicate between companies and customers. Frequently, the customer of a product or service sends complaints via social media. Customers' complaint data serve as a good suggestion for companies and organizations to improve their products and services. With the increasing number of customer complaints that have entered through social media accounts, government-owned drinking water companies need a more efficient way to extract information from complaint data. In this research, text mining is used to extract information about customer complaints against drinking water companies from social media Twitter. Latent Dirichlet Allocation (LDA) and self-organizing maps (SOM) approach is applied to model complaint topics and find out which are most frequently complained the test results indicate grouping the data into five classes is the most appropriate model. Pipes leakage are the most frequently reported topics, 27.8% of total datasets.

KW - LDA

KW - SOM

KW - clustering

KW - social media

KW - text mining

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U2 - 10.1109/EIConCIT50028.2021.9431931

DO - 10.1109/EIConCIT50028.2021.9431931

M3 - Conference contribution

AN - SCOPUS:85107260084

T3 - 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021

SP - 338

EP - 342

BT - 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021

A2 - Alfred, Rayner

A2 - Haviluddin, Haviluddin

A2 - Wibawa, Aji Prasetya

A2 - Santoso, Joan

A2 - Kurniawan, Fachrul

A2 - Junaedi, Hartarto

A2 - Purnawansyah, Purnawansyah

A2 - Setyati, Endang

A2 - Saurik, Herman Thuan To

A2 - Setiawan, Esther Irawati

A2 - Setyaningsih, Eka Rahayu

A2 - Pramana, Edwin

A2 - Kristian, Yosi

A2 - Kelvin, Kelvin

A2 - Purwanto, Devi Dwi

A2 - Kardinata, Eunike

A2 - Anugrah, Prananda

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd East Indonesia Conference on Computer and Information Technology, EIConCIT 2021

Y2 - 9 April 2021 through 11 April 2021

ER -

Dewinta A, Irawan MI. Customer Complaints Clusterization of Government Drinking Water Company on Social Media Twitter using Text Mining. In Alfred R, Haviluddin H, Wibawa AP, Santoso J, Kurniawan F, Junaedi H, Purnawansyah P, Setyati E, Saurik HTT, Setiawan EI, Setyaningsih ER, Pramana E, Kristian Y, Kelvin K, Purwanto DD, Kardinata E, Anugrah P, editors, 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 338-342. 9431931. (3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021). doi: 10.1109/EIConCIT50028.2021.9431931

Customer Complaints Clusterization of Government Drinking Water Company on Social Media Twitter using Text Mining

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Keywords

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