Social Media Predictive Analytics and Sentiment Modelling of Gig Economy in the Industrial Revolution 4.0 (IR4.0)

The emergence of the 'gig economy' was driven by the rise of the Fourth Industrial Revolution (IR4.0). According to the Nielson Global Share Community Report (2014), the gig economy was projected to be worth between US$10 to US$14 billion by 2025 in Malaysia. Additionally, 68% of the global population showed their interest in being part of the gig economy. Digital platforms like Airbnb, Grab, and Foodpanda were some of the most well-known examples. The gig economy offered many advantages such as flexibility, variety, independence, and lower cost. However, there were also issues linked to it like safety concerns, lack of regulations, uncertainty, and unstable streams of income. These resulted in netizens expressing their sentiments on Social Networking Sites (SNS), potentially hampering the growth of the gig economy. Predictive analytics through social media intelligence and sentiment modelling offered integral techniques for the analysis of subjective thoughts and latent perceptions in textual forms and linguistic settings. This research implemented these techniques to explore the sentiments of SNS users and developed predictive models to see how sentiments affected the intention of users in being a part of the gig economy. Consequently, the findings served as the basis of recommendations to firms and government bodies for the enhancement of the gig economy. In other words, the knowledge obtained from the sentiments and predictive models better guided respective bodies in devising both betterment and remedial strategies for the gig economy. Additionally, the findings addressed the safety, regulatory, and security issues associated with the gig economy. This research delivered up-to-date antecedents for the gig economy, which were useful for the Malaysian government's vision for it to be the new source of economic growth.