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)
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Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Sentiment analysis; Twitter; Machine learning; Natural language processing |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty/Division: | Institute of Postgraduate Studies Center for Mathematical Science |
| Depositing User: | Dr. Nor Azuana Ramli |
| Date Deposited: | 03 Jan 2023 06:22 |
| Last Modified: | 03 Jan 2023 06:22 |
| URI: | http://umpir.ump.edu.my/id/eprint/36019 |
| Download Statistic: | View Download Statistics |
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