Should I visit this place? Inclusion and Exclusion Phrase Mining from Reviews
This work provides a novel method for extracting specific inclusion/exclusion information from reviews, which can help travelers with unique needs (e.g., disabilities, dietary restrictions, or traveling with toddlers) make more informed decisions about visiting tourist spots. It is an incremental step in tourism data mining.
This paper addresses the problem of identifying inclusion and exclusion phrases from tourist reviews, focusing on 11 specific factors like disability or dietary preferences. Their broad-level classifier achieved an F1-score of approximately 80% for inclusion and 82% for exclusion, while a more granular 11-class classifier reached F1-scores of about 98% and 97% for inclusion and exclusion, respectively.
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.