Sentiment and semantic analysis: Urban quality inference using machine learning algorithms
Artikel i vetenskaplig tidskrift, 2024

Sustainable urban transformation requires comprehensive knowledge about the built environment, including people's perceptions, use of sites, and wishes. Qualitative interviews are conducted to understand better people's opinions about a specific topic or location. This study explores the automatization of the interview coding process by investigating how state-of-the-art natural language processing techniques classify sentiment and semantic orientation from interviews transcribed in Swedish. For the sentiment analysis, the Swedish bidirectional encoder representations from transformers (BERT) model KB-BERT was used to perform a multi-class classification task on a text sentence level into three different classes: positive, negative, and neutral. Named entity recognition (NER) and string search were used for the semantic analysis to perform multi-label classification to match domain-related topics to the sentence. The models were trained and evaluated on partially annotated datasets. The results demonstrate that the implemented deep learning techniques are a possible and promising solution to achieve the stated goal.

Urban planning

Artificial intelligence

Machine learning

Författare

Emily Ho

Student vid Chalmers

Göteborgs universitet

Michelle Schneider

Göteborgs universitet

Student vid Chalmers

Sanjay Somanath

Chalmers, Arkitektur och samhällsbyggnadsteknik, Byggnadsteknologi

Yinan Yu

Chalmers, Data- och informationsteknik, Funktionell programmering

Liane Thuvander

Chalmers, Arkitektur och samhällsbyggnadsteknik, Arkitekturens teori och metod

iScience

25890042 (eISSN)

Vol. 27 7 110192

Digital Twin Cities Centre

VINNOVA (2019-00041), 2020-02-29 -- 2024-12-31.

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Språkteknologi (språkvetenskaplig databehandling)

Datavetenskap (datalogi)

DOI

10.1016/j.isci.2024.110192

Mer information

Senast uppdaterat

2024-07-25