The jupyter notebook is developed to provide an analytics solution by analysing the reviews from Disneyland visitors in three Disneyland theme parks located in California, Paris and Hongkong. The insights obtained from the analysis are used to provide recommendations to Disneyland management to improve their services and customer satisfaction. The data is obtained from the TripAdvisor website. The notebook contains the analysis of reviews and a separate report is developed from the analysis findings.
The following points detail the workflow of this notebook:
- Finding the peak time for each theme park.
- Finding the major groups of visitors.
- Finding the theme park with the highest ratings.
- Performing sentiment analysis to discover visitors' sentiments towards theme park aspects.
- Detecting concerns and interests of visitors using topic modelling.
- Detecting the differences in concerns and interests of visitors between three parks.
- Finding the differences in concerns and visitors among major visitor groups.
- pandas
- numpy
- matplotlib
- sklearn
- nltk
- statistics
- collections
- re
- gensim
- seaborn
- wordcloud
- math
The notebook uses reviews from Disneyland visitors and performs an analysis of those texts. A similar kind of analysis on texts related to any topic can be undertaken to gather insights that may facilitate in taking further actions to improve the business.
Problems can be encountered while viewing some files, such as the notebook and dataset. Cloning the repository on your local machine allows you to view the contents of all files in this repository. On the browser, the data can be viewed in raw format only. It is recommended to obtain a copy of files in your local system so that viewing and running the notebook become smooth.
You may use any of Jupyter Notebook, Jupyter Lab or Google Colab to run the .ipynb file. The main thing to remember is you must store both the .ipynb notebook file and the .csv file in the same folder path. Lastly, before running the notebook, you may need all the above-mentioned required python libraries installed on your system, or you may install the libraries before executing the cell that calls any new libraries.