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Customer sentiment analysis through social media feedback: A case study on telecommunication company

Customer sentiment analysis is an automated way of detecting sentiments in online interactions in order to assess customer opinions about a product, brand or service. It assists companies in gaining insights and efficiently responding to their customers. This study presents a machine learning approa...

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Main Authors: Mat Zain, Siti Nur Syamimi, Ramli, Nor Azuana, Adnan, Rose Adzreen
Format: Article
Language:English
Published: Universiti Malaysia Pahang 2022
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/36019/1/2595.pdf
http://umpir.ump.edu.my/id/eprint/36019/
https://doi.org/10.15282/ijhtc.v7i2.8739
https://doi.org/10.15282/ijhtc.v7i2.8739
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spelling my.ump.umpir.360192023-01-03T06:22:32Z http://umpir.ump.edu.my/id/eprint/36019/ Customer sentiment analysis through social media feedback: A case study on telecommunication company Mat Zain, Siti Nur Syamimi Ramli, Nor Azuana Adnan, Rose Adzreen QA75 Electronic computers. Computer science Customer sentiment analysis is an automated way of detecting sentiments in online interactions in order to assess customer opinions about a product, brand or service. It assists companies in gaining insights and efficiently responding to their customers. This study presents a machine learning approach to analyse how sentiment analysis detects positive and negative feedback about a telecommunication company’s products. Customer feedback data were taken from Twitter through Streaming API (Application Programming Interface), where Tweets are retrieved in real time based on search terms, time, users and likes. Responses from the twitter API are parsed into tables and stored in a CSV file. Based on the analysis, it was found that there was no negative sentiment from the customers. The data were then split into training and testing to be tested on the three different supervised learning algorithms used in this study which are Support Vector Machine, Random Forest, and Naïve Bayes. Lasty, the performance of each model was compared to select the most accurate model and from the analysis, it can be concluded that Support Vector Machine gives the best performance in terms of accuracy, Mean Squared Error, Root Mean Squared Error and Area Under the ROC curve. Universiti Malaysia Pahang 2022-12 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/36019/1/2595.pdf Mat Zain, Siti Nur Syamimi and Ramli, Nor Azuana and Adnan, Rose Adzreen (2022) Customer sentiment analysis through social media feedback: A case study on telecommunication company. International Journal of Humanities Technology and Civilization, 7 (2). pp. 54-61. ISSN 2600-8815 (Online) 2289-7216 (Printed) https://doi.org/10.15282/ijhtc.v7i2.8739 https://doi.org/10.15282/ijhtc.v7i2.8739
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Mat Zain, Siti Nur Syamimi
Ramli, Nor Azuana
Adnan, Rose Adzreen
Customer sentiment analysis through social media feedback: A case study on telecommunication company
description Customer sentiment analysis is an automated way of detecting sentiments in online interactions in order to assess customer opinions about a product, brand or service. It assists companies in gaining insights and efficiently responding to their customers. This study presents a machine learning approach to analyse how sentiment analysis detects positive and negative feedback about a telecommunication company’s products. Customer feedback data were taken from Twitter through Streaming API (Application Programming Interface), where Tweets are retrieved in real time based on search terms, time, users and likes. Responses from the twitter API are parsed into tables and stored in a CSV file. Based on the analysis, it was found that there was no negative sentiment from the customers. The data were then split into training and testing to be tested on the three different supervised learning algorithms used in this study which are Support Vector Machine, Random Forest, and Naïve Bayes. Lasty, the performance of each model was compared to select the most accurate model and from the analysis, it can be concluded that Support Vector Machine gives the best performance in terms of accuracy, Mean Squared Error, Root Mean Squared Error and Area Under the ROC curve.
format Article
author Mat Zain, Siti Nur Syamimi
Ramli, Nor Azuana
Adnan, Rose Adzreen
author_facet Mat Zain, Siti Nur Syamimi
Ramli, Nor Azuana
Adnan, Rose Adzreen
author_sort Mat Zain, Siti Nur Syamimi
title Customer sentiment analysis through social media feedback: A case study on telecommunication company
title_short Customer sentiment analysis through social media feedback: A case study on telecommunication company
title_full Customer sentiment analysis through social media feedback: A case study on telecommunication company
title_fullStr Customer sentiment analysis through social media feedback: A case study on telecommunication company
title_full_unstemmed Customer sentiment analysis through social media feedback: A case study on telecommunication company
title_sort customer sentiment analysis through social media feedback: a case study on telecommunication company
publisher Universiti Malaysia Pahang
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/36019/1/2595.pdf
http://umpir.ump.edu.my/id/eprint/36019/
https://doi.org/10.15282/ijhtc.v7i2.8739
https://doi.org/10.15282/ijhtc.v7i2.8739
_version_ 1754529721346949120
score 13.222552

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