Motivation Behind This Project:
I’ve always been interested in what drives people to stay, come back, or drop off — especially when it comes to experience-based brands like Disneyland. I wanted to explore how real visitor emotions, frustrations, and highlights could translate into better lifecycle and engagement strategy. Analyzing reviews across Disneyland’s Paris, California, and Hong Kong parks felt like the perfect way to dig into unfiltered sentiment and uncover what matters most to different audiences.
What I did:
I sourced a dataset from Kaggle with over 40,000 reviews across Disneyland’s Paris, California, and Hong Kong locations, posted between 2010 and 2019. My goal was to translate raw visitor feedback into actionable marketing insights.
Used NLP tools (TextBlob, Sentiment Analyzer) to evaluate visitor sentiment and emotional tone
Ran word frequency analysis to uncover commonly mentioned topics across locations
Analyzed review trends by country to surface cultural patterns in visitor expectations and satisfaction
Visualized seasonal sentiment shifts using Matplotlib to inform potential campaign timing
Link to the code: DisneyLand Reviews Analysis