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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| Format: | Article |
| Language: | English |
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Universiti Malaysia Pahang
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/36019/ http://umpir.ump.edu.my/id/eprint/36019/1/2595.pdf |
| _version_ | 1848824930588688384 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:20:51Z |
| format | Article |
| id | ump-36019 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:20:51Z |
| publishDate | 2022 |
| publisher | Universiti Malaysia Pahang |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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). (Published) https://doi.org/10.15282/ijhtc.v7i2.8739 https://doi.org/10.15282/ijhtc.v7i2.8739 |
| 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 |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/36019/ http://umpir.ump.edu.my/id/eprint/36019/ http://umpir.ump.edu.my/id/eprint/36019/ http://umpir.ump.edu.my/id/eprint/36019/1/2595.pdf |