Understanding the sentiment on gig economy: good or bad?
The gig economy offers many advantages, such as flexibility, variety, independence, and lower cost. However, there are also safety concerns, lack of regulations, uncertainty, and unsatisfactory services, causing people to voice their opinion on social media. This paper aims to explore the sentiments...
| Main Authors: | , , , |
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| Format: | Article |
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Korea Distribution Science Association
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/102535/ |
| _version_ | 1848863822835613696 |
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| author | Norazmi, Fatin Aimi Naemah Mazlan, Nur Syazwani Said, Rusmawati Ok Rahmat, Rahmita Wirza |
| author_facet | Norazmi, Fatin Aimi Naemah Mazlan, Nur Syazwani Said, Rusmawati Ok Rahmat, Rahmita Wirza |
| author_sort | Norazmi, Fatin Aimi Naemah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The gig economy offers many advantages, such as flexibility, variety, independence, and lower cost. However, there are also safety concerns, lack of regulations, uncertainty, and unsatisfactory services, causing people to voice their opinion on social media. This paper aims to explore the sentiments of consumers concerning gig economy services (Grab, Foodpanda and Airbnb) through the analysis of social media. First, Vader Lexicon was used to classify the comments into positive, negative, and neutral sentiments. Then, the comments were further classified into three machine learning algorithms: Support Vector Machine, Light Gradient Boosted Machine, and Logistic Regression. Results suggested that gig economy services in Malaysia received more positive sentiments (52%) than negative sentiments (19%) and neutral sentiments (29%). Based on the three algorithms used in this research, LGBM has been the best model with the highest accuracy of 85%, while SVM has 84% and LR 82%. The results of this study proved the power of text mining and sentiment analysis in extracting business value and providing insight to businesses. Additionally, it aids gig managers and service providers in understanding clients' sentiments about their goods and services and making necessary adjustments to optimize satisfaction. |
| first_indexed | 2025-11-15T13:39:02Z |
| format | Article |
| id | upm-102535 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:39:02Z |
| publishDate | 2022 |
| publisher | Korea Distribution Science Association |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1025352024-03-12T08:56:28Z http://psasir.upm.edu.my/id/eprint/102535/ Understanding the sentiment on gig economy: good or bad? Norazmi, Fatin Aimi Naemah Mazlan, Nur Syazwani Said, Rusmawati Ok Rahmat, Rahmita Wirza The gig economy offers many advantages, such as flexibility, variety, independence, and lower cost. However, there are also safety concerns, lack of regulations, uncertainty, and unsatisfactory services, causing people to voice their opinion on social media. This paper aims to explore the sentiments of consumers concerning gig economy services (Grab, Foodpanda and Airbnb) through the analysis of social media. First, Vader Lexicon was used to classify the comments into positive, negative, and neutral sentiments. Then, the comments were further classified into three machine learning algorithms: Support Vector Machine, Light Gradient Boosted Machine, and Logistic Regression. Results suggested that gig economy services in Malaysia received more positive sentiments (52%) than negative sentiments (19%) and neutral sentiments (29%). Based on the three algorithms used in this research, LGBM has been the best model with the highest accuracy of 85%, while SVM has 84% and LR 82%. The results of this study proved the power of text mining and sentiment analysis in extracting business value and providing insight to businesses. Additionally, it aids gig managers and service providers in understanding clients' sentiments about their goods and services and making necessary adjustments to optimize satisfaction. Korea Distribution Science Association 2022-12 Article PeerReviewed Norazmi, Fatin Aimi Naemah and Mazlan, Nur Syazwani and Said, Rusmawati and Ok Rahmat, Rahmita Wirza (2022) Understanding the sentiment on gig economy: good or bad? Journal of Asian Finance, Economics and Business, 9 (10). 0189-0200. ISSN 2288-4637; ESSN: 2288-4645 https://koreascience.kr/article/JAKO202236434502519.view?orgId=kodisa&hide=breadcrumb,journalinfo 10.13106/jafeb.2022.vol9.no10.0189 |
| spellingShingle | Norazmi, Fatin Aimi Naemah Mazlan, Nur Syazwani Said, Rusmawati Ok Rahmat, Rahmita Wirza Understanding the sentiment on gig economy: good or bad? |
| title | Understanding the sentiment on gig economy: good or bad? |
| title_full | Understanding the sentiment on gig economy: good or bad? |
| title_fullStr | Understanding the sentiment on gig economy: good or bad? |
| title_full_unstemmed | Understanding the sentiment on gig economy: good or bad? |
| title_short | Understanding the sentiment on gig economy: good or bad? |
| title_sort | understanding the sentiment on gig economy: good or bad? |
| url | http://psasir.upm.edu.my/id/eprint/102535/ http://psasir.upm.edu.my/id/eprint/102535/ http://psasir.upm.edu.my/id/eprint/102535/ |