Understanding the Sentiment on Gig Economy: Good or Bad?

• 영문명: Understanding the Sentiment on Gig Economy: Good or Bad?
• 발행기관: 한국유통과학회
• 저자명: Fatin Aimi Naemah NORAZMI Nur Syazwani MAZLAN Rusmawati SAID Rahmita Wirza OK RAHMAT
• 간행물 정보: 『The Journal of Asian Finance, Economics and Business(JAFEB)』Vol. 9 No.10, 189~200쪽, 전체 12쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2022.12.31

영문 초록

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.

목차

1. Introduction

2. Literature Review

3. Methodology

4. Results and Discussion

5. Conclusion

References

키워드

Industrial Revolution 4.0 Gig Economy Social Media Sentiment Analysis