Classifying Travel-related Intents in Textual Data

Abstract

Intent classification refers to the process of identifying a set of intents of interest that appear in a given document. This work considers the task of annotating travel-related reviews with travel intents that best represent the reviewer's reason for visiting the place of interest (POI). A domain-tailored word embedding model is learned to construct intent-specific feature vectors, thereby improving classification accuracy. The feasibility of multiclass intent classification is explored using an intent corpus, consisting of 6,560 labelled reviews.