| Autor: |
Emily Ho, Michelle Schneider, Sanjay Somanath, Yinan Yu, Liane Thuvander |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
iScience, Vol 27, Iss 7, Pp 110192- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2589-0042 |
| DOI: |
10.1016/j.isci.2024.110192 |
| Popis: |
Summary: 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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