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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.

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|July 19, 2024
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Summary
This summary is machine-generated.

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.

Keywords:
Artificial intelligenceMachine learningUrban planning

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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.