Extracting Place Functionality From Crowdsourced Textual Data Using Semantic Space Modeling

Place has gained significant attention in geographic information science. Places are described by users that make a huge amount of user-generated textual contents. This research introduces a novel approach to extract place functionality using crowdsourcing textual data, which are shared in the form of online reviews. To achieve this goal, salient features are modeled as directions in a domain-specific semantic space. We propose an unsupervised method that only requires a Bag-of-Words (BoW) of place reviews and utilizes Natural Language Processing (NLP) methods. Finally, a probabilistic multi-label functionality for each place is predicted using the semantic space constructed based on the salient feature directions, and the maximum probability is defined as the main functionality of place. The functionality of ‘Hotels’ is determined with an average accuracy of 88.52%, while the efficiency of extracting ‘Attractions’, ‘FoodPlaces’, and ‘Shoppings’ functionalities is 65.66%, 64.99%, and 12.70%, respectively. The proposed method can help users to find places that afford a specific functionality and can improve decisions in urban planning.