Natural Language Processing, Sentiment Analysis, and Urban Studies: A Systematic Review

Natural Language Processing, Sentiment Analysis, and Urban Studies: A Systematic Review

Consalter Diniz, Maria Luisa; Polverini Boeing, Lais; dos Santos Carvalho, Wendel; Bertola Duarte, Rovenir

Full Article:

This paper discusses the potential of using data from social media and location data platforms to create cartographies that enhance our understanding of urban dynamics. Natural Language Processing (NLP) and sentiment analysis are highlighted as essential tools for comprehending and categorizing this data. The study conducted a systematic review of NLP and sentiment analysis applications in urban studies, covering 27 peer-reviewed journals and conference papers published between 2018 and 2023. The research classified applications into six categories: urban livability, governance and management, user and landscape perception, land use and zoning, public health, and transportation and mobility. Most studies primarily relied on data from social media platforms like Twitter and location data sources such as Google Maps and Trip Advisor. Challenges include dealing with irrelevant or misleading information in publicly available data and limited accuracy when analyzing sentiments of non-English-speaking populations.

This paper discusses the potential of using data from social media and location data platforms to create cartographies that enhance our understanding of urban dynamics. Natural Language Processing (NLP) and sentiment analysis are highlighted as essential tools for comprehending and categorizing this data. The study conducted a systematic review of NLP and sentiment analysis applications in urban studies, covering 27 peer-reviewed journals and conference papers published between 2018 and 2023. The research classified applications into six categories: urban livability, governance and management, user and landscape perception, land use and zoning, public health, and transportation and mobility. Most studies primarily relied on data from social media platforms like Twitter and location data sources such as Google Maps and Trip Advisor. Challenges include dealing with irrelevant or misleading information in publicly available data and limited accuracy when analyzing sentiments of non-English-speaking populations.

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DOI: 10.5151/sigradi2023-375

Referências bibliográficas

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Como citar:

Consalter Diniz, Maria Luisa; Polverini Boeing, Lais; dos Santos Carvalho, Wendel; Bertola Duarte, Rovenir; "Natural Language Processing, Sentiment Analysis, and Urban Studies: A Systematic Review", p-1740-1751. In: . São Paulo: Blucher, 2024.
ISSN 23186968, DOI 10.5151/sigradi2023-375