  Although several automatic itinerary generation services have made travel
planning easy, often times travellers find themselves in unique situations
where they cannot make the best out of their trip. Visitors differ in terms of
many factors such as suffering from a disability, being of a particular dietary
preference, travelling with a toddler, etc. While most tourist spots are
universal, others may not be inclusive for all. In this paper, we focus on the
problem of mining inclusion and exclusion phrases associated with 11 such
factors, from reviews related to a tourist spot. While existing work on tourism
data mining mainly focuses on structured extraction of trip related
information, personalized sentiment analysis, and automatic itinerary
generation, to the best of our knowledge this is the first work on
inclusion/exclusion phrase mining from tourism reviews. Using a dataset of 2000
reviews related to 1000 tourist spots, our broad level classifier provides a
binary overlap F1 of $\sim$80 and $\sim$82 to classify a phrase as inclusion or
exclusion respectively. Further, our inclusion/exclusion classifier provides an
F1 of $\sim$98 and $\sim$97 for 11-class inclusion and exclusion classification
respectively. We believe that our work can significantly improve the quality of
an automatic itinerary generation service.
