This study aims to provide such detailed insights into the effect of COVID-19 on customer behaviours, which Airbnb can utilise to make necessary modifications to its services to meet customer needs based on their perception coming out of the pandemic. Ultimately, the significant textual predictors will connect changes in customer attitudes and preferences and provide predictive recommendations.
- RStudio
- RSQLite
- Statistical Analysis
- T-test & Difference testing
- Bag-of-words Analysis
- Sentiment Analysis
- Topic Mining
- Predictive Analysis
- Machine Learning
Airbnb has a publicly available repository of data on “Inside Airbnb” website, which records monthly snapshots of Airbnb datasets. The data for 16 cities from various parts of the world are collected based on the popularity of the cities for tourism. The rationale is that the effects of pandemic, as seen in the most popular cities, will impact Airbnb the most as well and hence, the change in customer attitudes from these cities will be representative of the expected impact to be seen in other regions where Airbnb has active operations. The selected cities for this study are:
- Amsterdam 2. Bangkok 3. Barcelona 4. Hong Kong 5. Istanbul 6. London 7. Melbourne 8. New York 9. Paris 10. Porto 11. Rome 12. San Francisco 13. Singapore 14. Sydney 15. Madrid 16. Vienna
The project has been conducted on local drive upon completion of data collection
Project is: complete
- Further data could be utilized in the analysis for better outcomes. This could entail the inclusion of other cities.
- Financial and media data can be assimmilated to enhance the scope of the project.
- Database interaction with R could be utilized to enhance the management of the quantity of data.