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Published on: May 7, 2019
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Sentiment and semantic analysis: Urban quality inference using machine learning algorithms
Emily Ho1,2, Michelle Schneider1,2, Sanjay Somanath3
1Department of Computer Science and Engineering, University of Gothenburg, Universitetsplatsen 1, 405 30 Gothenburg, Sweden.
Iscience
|July 19, 2024
Summary
This study automates interview coding for urban planning using natural language processing. Deep learning models accurately classify sentiment and identify topics in Swedish interviews, aiding sustainable urban transformation.
Area of Science:
- Urban Planning and Design
- Computational Linguistics
- Artificial Intelligence
Background:
- Sustainable urban transformation necessitates understanding public perceptions of the built environment.
- Qualitative interviews are crucial for gathering insights into people's opinions and site usage.
- Manual coding of interview data is time-consuming and resource-intensive.
Purpose of the Study:
- To explore the automation of qualitative interview coding using advanced natural language processing (NLP) techniques.
- To investigate the effectiveness of NLP models in classifying sentiment and semantic orientation in Swedish interview transcripts.
- To assess the potential of deep learning for efficient analysis of qualitative data in urban studies.
Main Methods:
- Utilized a Swedish bidirectional encoder representations from transformers (BERT) model, KB-BERT, for sentiment analysis (positive, negative, neutral classification).
- Employed Named Entity Recognition (NER) and string search for semantic analysis, enabling multi-label topic classification.
- Trained and evaluated NLP models on partially annotated Swedish interview datasets.
Main Results:
- Demonstrated that deep learning techniques can effectively automate the classification of sentiment in transcribed interviews.
- Showcased the capability of NLP models to identify and categorize domain-related topics within the text.
- Achieved promising results in classifying sentiment and semantic orientation, indicating the feasibility of automated coding.
Conclusions:
- The study confirms that state-of-the-art NLP techniques offer a viable and promising solution for automating the coding of qualitative interviews.
- Automated analysis can significantly enhance the efficiency of gathering and processing public perception data for urban planning.
- This approach supports more comprehensive knowledge acquisition for sustainable urban transformation initiatives.
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