情感和语义分析:使用机器学习算法推断城市质量
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
概括
这项研究使用自然语言处理自动化了城市规划采访编码. 深度学习模型准确地分类情绪,并在瑞典采访中确定主题,帮助可持续的城市转型.
科学领域:
- 城市规划和设计
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 可持续的城市转型需要了解公众对建筑环境的看法.
- 定性访谈对于收集人们的意见和网站使用的见解至关重要.
- 采访数据的手动编码是耗时且资源密集的.
研究的目的:
- 探索使用先进的自然语言处理 (NLP) 技术进行定性面试编码的自动化.
- 调查NLP模型在分类瑞典访谈成绩单中的情绪和语义导向方面的有效性.
- 评估深度学习的潜力,以有效地分析城市研究中的定性数据.
主要方法:
- 利用来自变压器 (BERT) 模型的瑞典双向编码器表示,KB-BERT,进行情绪分析 (正,负,中性分类).
- 雇员命名实体识别 (NER) 和字符串搜索用于语义分析,使多标签主题分类成为可能.
- 在部分注释的瑞典访谈数据集上训练和评估NLP模型.
主要成果:
- 证明深度学习技术可以有效地自动化转录面试中的情绪分类.
- 展示了NLP模型在文本中识别和分类领域相关主题的能力.
- 在分类情绪和语义导向方面取得了有希望的结果,表明自动编码的可行性.
结论:
- 该研究证实,最先进的NLP技术为自动化定性访谈编码提供了可行和有前途的解决方案.
- 自动化分析可以显著提高城市规划收集和处理公众感知数据的效率.
- 这种方法支持为可持续的城市转型倡议获得更全面的知识.
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