Web Analytics
In the realm of social media analytics, I explore how unstructured user-generated content can be transformed into strategic business intelligence. My research focuses on bridging the gap between vast amounts of consumer feedback and corporate decision-making. By employing advanced techniques such as multimodal sentiment analysis and dynamic topic modeling, I aim to uncover evolving customer trends and provide data-driven insights that optimize operational effectiveness and sustain brand loyalty.
Fitness, Feedback, and the Future: Understanding PureGym Users Through Social Media Analytics
This study investigates consumer sentiment and operational dynamics at PureGym by applying social media analytics to Google Maps reviews. Integrating a multimodal sentiment analysis model based on BERT with dynamic topic modelling, the research reveals evolving user trends that traditional ratings often obscure. The analysis indicates that while 70% of reviews remain positive, user satisfaction consistently declines as gym locations age. Positive feedback is primarily driven by staff interactions and parking availability, whereas persistent complaints center on hygiene, specifically regarding odours and facility maintenance. These findings highlight critical areas of operational fatigue and provide data-driven insights for optimizing service quality and sustaining customer loyalty.