Predictive promoter part 4 – text mining and conclusions

In this final part of  our predictive promoter video series, Jarlath Quinn tackles the project’s most ambitious task: mining the open-ended guest comments to uncover important insights. Here you can discover how we can use text analytics to extract a series of concepts and sentiments from customer comments in order to categorise the guests’ responses.

By visualizing the relationships between these key concepts we are able to see what aspects of the guests experiences are most strongly correlated with positive evaluations. Moreover, by categorising the responses we are able to use the text data to build a model that reveals the hierarchy of factors associated with weaker or stronger recommendation scores.

In the final example, Jarlath shows how we can combine the original operational and transactional data with the guests’ open-ended comments to show the interaction between transactional factors such as spending or discount and ‘soft’ factors such as hotel cleanliness or noise levels.

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