从基于BERT的在线审查中识别场景特征.
1School of Business Administration, Northeastern University, Shenyang, Liaoning Province, China.
PeerJ. Computer science
|June 22, 2023
概括
本研究介绍了一种基于BERT的模型,用于自动识别在线评论中的产品使用场景和特征. 这种方法克服了传统方法的局限性,通过了解特定环境中的客户需求来帮助产品开发.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 产品开发 产品开发
背景情况:
- 产品设计越来越依赖于了解特定的使用场景.
- 传统的场景识别方法是主观的,昂贵的,缺乏细节性.
- 有效的场景特征关联对于具有竞争力的产品开发至关重要.
研究的目的:
- 提出一种基于BERT的新型模型,用于从在线用户评论中自动识别场景特征.
- 克服现有方法在主观性,成本和细节性方面的局限性.
- 为产品开发人员提供一个工具,以便在特定的使用环境中更好地了解客户需求.
主要方法:
- 开发基于BERT的场景特征识别框架.
- 基于BERT的场景句子识别模型的构建.
- 使用Skip-gram和词向量相似性来创建场景和功能词典.
- 分析场景特征并发矩阵,以将场景与产品特征关联起来.
主要成果:
- 拟议的模型有效地从在线评论中提取用户体验和使用场景信息.
- 该方法证明了将产品特征与特定场景联系起来具有实际价值.
- 太平洋汽车评论的实验验证证证了该模型的有效性.
结论:
- 基于BERT的模型提供了一个客观和细分的方法来确定场景特征.
- 这种方法显著提高了在特定产品使用背景下对客户需求的理解.
- 这些发现对改善产品开发策略和客户满意度有直接影响.
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