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id cf2017_101
authors Chen, Nai Chun; Zhang, Yan; Stephens, Marrisa; Nagakura, Takehiko; Larson, Kent
year 2017
title Urban Data Mining with Natural Language Processing: Social Media as Complementary Tool for Urban Decision Making
source Gülen Çagdas, Mine Özkar, Leman F. Gül and Ethem Gürer (Eds.) Future Trajectories of Computation in Design [17th International Conference, CAAD Futures 2017, Proceedings / ISBN 978-975-561-482-3] Istanbul, Turkey, July 12-14, 2017, pp. 101-109.
summary The presence of web2.0 and traceable mobile devices creates new opportunities for urban designers to understand cities through an analysis of user-generated data. The emergence of “big data” has resulted in a large amount of information documenting daily events, perceptions, thoughts, and emotions of citizens, all annotated with the location and time that they were recorded. This data presents an unprecedented opportunity to gauge public opinion about the topic of interest. Natural language processing with social media is a novel tool complementary to traditional survey methods. In this paper, we validate these methods using tourism data from Trip-Advisor in Andorra. “Natural language processing” (NLP) detects patterns within written languages, enabling researchers to infer sentiment by parsing sentences from social media. We applied sentiment analysis to reviews of tourist attractions and restaurants. We found that there were distinct geographic regions in Andorra where amenities were reviewed as either uniformly positive or negative. For example, correlating negative reviews of parking availability with land use data revealed a shortage of parking associated with a known traffic congestion issue, validating our methods. We believe that the application of NLP to social media data can be a complementary tool for urban decision making.
keywords Short Paper, Urban Design Decision Making, Social Media, Natural Language Processing
series CAAD Futures
email
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