AirbnbAI approached you again to develop a RapidMiner process
capable of analysing customer feelings (sentiment) about their stay
in one of the Sydney Airbnb rental properties.
AirbnbAI sent you a data set of 36,000 rental listings and the text of
548,000 reviews across 38 Sydney neighbourhoods. The provided
information has been partially cleaned up and includes a variety of
numerical, nominal and text attributes, description of which can be
found on the Inside Airbnb web site (source to upper-right).
AirbnbAI would like you to use RapidMiner to analyse (mainly) text
contained in the data set. AirbnbAI technical advisers suggested to
address the following issues and provided some helpful hints:
A) Is there a significant discrepancy between the sentiment of a property host and of
the customers? And if so, in which property-types and neighbourhoods is this
most pronounced? (use Operator Toolbox sentiment tools, Join and Aggregate)
B) What property groups can be identified purely from their textual description, what
are their characteristics and the recent (i.e. 2020) sentiment of customers?
(use text mining, sentiment analysis, data clustering and segmentation analysis)
C) Can the customer sentiment be predicted for the newly listed properties purely by
looking at their text description? If not what other aspects of the rental property
need to be also considered? (use an estimation model)
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