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Social Network Analysis and Mining
Published in:

01-12-2020 | Review Paper

Automatic extraction of urban outdoor perception from geolocated free texts

Authors: Frances A. Santos, Thiago H. Silva, Antonio A. F. Loureiro, Leandro A. Villas

Published in: Social Network Analysis and Mining | Issue 1/2020

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Abstract

The automatic extraction of urban perception shared by people on location-based social networks (LBSNs) is an important multidisciplinary research goal. One of the reasons is it facilitates the understanding of the intrinsic characteristics of urban areas in a scalable way, helping to leverage new services. However, content shared on LBSNs is diverse, encompassing several topics, such as politics, sports, culture, religion, and urban perceptions, making the task of content extraction regarding a particular topic very challenging. Considering free-text messages shared on LBSNs, we propose an automatic and generic approach to extract people’s perceptions. For that, our approach explores opinions that are spatiotemporal and semantically similar. We exemplify our approach in the context of urban outdoor areas in Chicago, New York City and London. Studying those areas, we found evidence that LBSN data bring valuable information about urban regions. To analyze and validate our outcomes, we conducted a temporal analysis to measure the results’ robustness over time. We show that our approach can be helpful to better understand urban areas considering different perspectives. We also conducted a comparative analysis based on a public dataset, which contains volunteers’ perceptions regarding urban areas expressed in a controlled experiment. We observe that both results yield a very similar level of agreement.
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Appendix
Available only for authorised users
Footnotes
2
https://​pt.​foursquare.​com/​trip-tips. Last accessed on September 12, 2020.
 
3
Foursquare Venue Category Hierarchy: https://​developer.​foursquare.​com/​docs/​build-with-foursquare/​categories/​. Last accessed on September 12, 2020.
 
4
Google Places Category: https://​developers.​google.​com/​places/​web-service/​supported_​types. Last accessed on September 12, 2020.
 
5
https://​www.​nltk.​org/​howto/​stem.​html. Last accessed on September 12, 2020.
 
6
http://​www.​nltk.​org/​howto/​wordnet.​html. Last accessed on September 12, 2020.
 
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Metadata
Title
Automatic extraction of urban outdoor perception from geolocated free texts
Authors
Frances A. Santos
Thiago H. Silva
Antonio A. F. Loureiro
Leandro A. Villas
Publication date
01-12-2020
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2020
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-020-00702-2

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