A natural language processing framework for collecting, analyzing, and visualizing users’ sentiment on the built environment: case implementation of New York City and Seoul residences-Reference-Cited by-同舟云学术

A natural language processing framework for collecting, analyzing, and visualizing users’ sentiment on the built environment: case implementation of New York City and Seoul residences

Published:2022-03-22 Issue:4 Volume:65 Page:278-294
ISSN:0003-8628
Container-title:Architectural Science Review
language:en
Short-container-title:Architectural Science Review

Author:

Chang Sun Woo¹,Rhee Deuk Young¹,Jun Han Jong²

Affiliation:

1. Research and Development Center, GARAM Architects & Associates, Seoul, South Korea

2. School of Architecture, Hanyang University, Seoul, South Korea

Funder

National Research Foundation of Korea(NRF) funded by the Korea government

Publisher

Informa UK Limited

Subject

Architecture

Link

https://www.tandfonline.com/doi/pdf/10.1080/00038628.2022.2050180

Reference43 articles.

1. Twitter Sentiment Analysis on Activities of Saudi General Entertainment Authority

2. Baziotos, C., N. Pelekis, and C. Doulkeridis. 2017. “DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis” Proceedings of the 11th International Workshop on Semantic Evaluations, 747-754.

3. Learning long-term dependencies with gradient descent is difficult

4. Deep learning-based natural language sentiment classification model for recognizing users’ sentiments toward residential space

5. Natural language processing

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