Using Classification Technique for Customer Relationship Management based on Thai Social Media Data

Chumwatana, Todsanai; Wongkolkitsilp, Karnsiree

doi:10.1145/3313991.3314010

Cited by 5 publications

(16 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results from the reviewed papers and articles show different SMA methods and tools. Most of the reviewed articles demonstrate the usage of sentiment analysis in different areas 54.54% of the articles reviewed uses sentiment analysis Su and Chen (2016), Vorvoreanu et al (2013), Chang et al (2017), Xiang et al (2016), Park et al (2016, He et al (2017), Stieglitz (2012, Anyanwu (2019), Shang et al (2018), Dong et al (2013,), Xu et al (2019), Dahal et al (2019), Kannan et al (2018), Martinez et al(2019), Alamsyah (2017), Barrelet et al (2016), Chen (2016), Chumwatana and Wongkolkitsilp (2019) Kannan et al (2018), Hu et al (2011), and Sachdeva and Mc Caffrey (2018, clustering technique 6.81% Jansen et al (2018), Myaeng et al (2016), and Ghosh et al (2017 , natural language processing 6.81% Barrelet et al (2016), Al Kubaizi et al (2015), and Saravan and Perepu ( 2019), text analysis 4.54% Dias et al (2018), andSingh et al (2018), event detection tool 2.27% Weiler (2013) and social network analysis 6.81% Alamsyah (2017), Udanor et al (2016), and Rahmani et al (2013).…”

Section: Sma Methods and Tools Usedmentioning

confidence: 99%

“…The study uses sentiment analysis technique and Tweetriz social media analytics tool. Chumwatana and Wongkolkitsilp (2019) introduces the study which uses social media analytics techniques sentiment analysis and classification tools like support vector machine and Naïve Bayes to social media platform twitter, facebook and Youtube to classify customers based on social media comments. Tian et al (2019) studied the application of twitter data to predict the services quality in the airline industry.…”

Section: Summary Of Reviewed Articlesmentioning

confidence: 99%

“…The most usable platform for social media analytics was twitter. 77.27% of the reviewed articles use twitter as the social media platform and the study found that most of the analytics performed in twitter platform was sentiment analysis; Su and Chen (2016), Vorvoreanu et al (2013), Bukhari et al (2012), Xiang et al (2016), Park et al (2016), Stieglitz (2012), Myaeng (2016, Weiler (2013), Anyanwu (2019), Shang et al (2018), Dong et al (2019), Xu et al (2019), Dahal et al (2019), Singh et al (2018), Kannan et al (2018), Culotta (2010), Martinez et al (2019), Al Kubaizi et al (2015), Saravan and Perepu (2019), Alamsyah (2017), Barrelet et al (2016), Chen (2016), Chumwatana and Wongkolkitsilp (2019), Tian et al (2019), He et al (2019), Hu et al (2011), Sachdeva andMc Caffrey (2018), Udanor et al (2016), Wu et al (2018), Santander et al (2020, Ghosh et al (2017), Araya et al (2017, Rahmani et al (2013), andVon Hoffen et al (2019). 18.18% of the reviewed articles used Facebook; Vorvoreanu et al (2013), Bukhari et al (2012), Dias et al (2018), Jain et al (2015, Saravan and Perepu (2019), Chumwatana and Wongkolkitsilp (2019), Cvijikj andMi...…”

Section: Types Of Sm Platforms Used For Smamentioning

confidence: 99%

“…18.18% of the reviewed articles used Facebook; Vorvoreanu et al (2013), Bukhari et al (2012), Dias et al (2018), Jain et al (2015, Saravan and Perepu (2019), Chumwatana and Wongkolkitsilp (2019), Cvijikj andMichaheliles (2011), andNavarro andRodriguez (2019). Apart from twitter and facebook other platforms used were Youtube (Jansen et al, 2018), and Chumwatana and Wongkolkitsilp (2019) (Weiler et al, 2013) and Tripadvisor 4.54% (Chang et al, 2017).…”

Section: Types Of Sm Platforms Used For Smamentioning

confidence: 99%

“…9.09% of the reviewed articles shows the use of SMA in disaster management;Dong et al (2013),Xu et al (2019),Sachdeva and Mc Caffrey (2018), andGhosh et al (2017). The studies show how SMA can be used to analyze social media data to get insight of different matters during disaster.Business also is among the fields which apply SMA 20.45% of the reviewed articles; Alamsyah (2017),Barrelet et al (2016), Chen (2016),Chumwatana and Wongkolkitsilp (2019),Tian et al (2019),He et al (2019),Cvijikj and Michaheliles (2011), Wu et al (2018), andRahmani et al (2013) studies demonstrate the usage of social media analytics tool to generate valuable information from the social media data.…”

mentioning

confidence: 99%

See 4 more Smart Citations

A Review of Usage and Applications of Social Media Analytics

Madila¹,

Dida²,

Kaijage³

2021

J INFORM SYSTEMS ENG

View full text Add to dashboard Cite

This paper presents the report of a social media analytics (SMA) review. The review conducted to find out the methods and tools used in social media analytics, types of social media platforms which the SMA are performed and the field which SMA has been performed. Social media contains a lot of user uploaded data in different formats like text, images, photos, video etc. These large volumes of data are converted in meaningful information which can be understood using different methods and tools which are called social media analytics. A literature review of articles published between 2010-2020 has been conducted using articles obtained from reputable databases IEEE Xplore, ACM digital, Emerald insight, Springer Link and Science direct. A number of 44 articles have been selected for review from 110 retrieved papers. The paper has been reviewed according to the study objectives. The study found that SMA tools and techniques which have been used are sentiment analysis, youtube analytics, visible intelligence, IBM Watson tool and predictive models. The social media platforms which were mostly used are twitter, facebook, youtube, trip advisor and blogs. SMA has been observed in different fields like agriculture, politics, health, social and business sector.

show abstract

Section: Sma Methods and Tools Usedmentioning

confidence: 99%

Section: Summary Of Reviewed Articlesmentioning

confidence: 99%

Section: Types Of Sm Platforms Used For Smamentioning

confidence: 99%

Section: Types Of Sm Platforms Used For Smamentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

A Review of Usage and Applications of Social Media Analytics

Madila¹,

Dida²,

Kaijage³

2021

J INFORM SYSTEMS ENG

View full text Add to dashboard Cite

show abstract

A Survey of Artificial Intelligence Techniques for User Perceptions’ Extraction from Social Media Data

Shaikh

Yayilgan

Zoto

et al. 2022

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

What social media sentiment tells us about why customers churn

Lappeman

Franco

Warner

et al. 2022

JCM

View full text Add to dashboard Cite

Purpose This study aims to investigate the factors that influence South African customers to potentially switch from one bank to another. Instead of using established models and survey techniques, the research measured social media sentiment to measure threats to switch. Design/methodology/approach The research involved a 12-month analysis of social media sentiment, specifically customer threats to switch banks (churn). These threats were then analysed for co-occurring themes to provide data on the reasons customers were making these threats. The study used over 1.7 million social media posts and focused on all five major South African retail banks (essentially the entire sector). Findings This study concluded that seven factors are most significant in understanding the underlying causes of churn. These are turnaround time, accusations of unethical behaviour, billing or payments, telephonic interactions, branches or stores, fraud or scams and unresponsiveness. Originality/value This study is unique in its measurement of unsolicited social media sentiment as opposed to most churn-related research that uses survey- or customer-data-based methods. In addition, this study observed the sentiment of customers from all major retail banks across 12 months. To date, no studies on retail bank churn theory have provided such an extensive perspective. The findings contribute to Susan Keaveney’s churn theory and provide a new measurement of switching threat through social media sentiment analysis.

show abstract

Using Classification Technique for Customer Relationship Management based on Thai Social Media Data

Cited by 5 publications

References 9 publications

A Review of Usage and Applications of Social Media Analytics

A Review of Usage and Applications of Social Media Analytics

A Survey of Artificial Intelligence Techniques for User Perceptions’ Extraction from Social Media Data

What social media sentiment tells us about why customers churn

Contact Info

Product

Resources

About