Huang Xunjiang, Yan Kang
School of Business Administration, Northeastern University, Shenyang, Liaoning Province, China.
PeerJ Comput Sci. 2023 May 22;9:e1398. doi: 10.7717/peerj-cs.1398. eCollection 2023.
Scenario endows a product with meanings. It has become the key to win the competition to design a product according to specific usage scene. Traditional scenario identification and product feature association methods have disadvantages such as subjectivity, high cost, coarse granularity, and limited scenario can be identified. In this regard, we propose a BERT-based scenario-feature identification model to effectively extract the information about users' experience and usage scene from online reviews. First, the scenario-feature identification framework is proposed to depict the whole identification process. Then, the BERT-based scene-sentence recognition model is constructed. The Skip-gram and word vector similarity methods are used to construct the scene and feature lexicon. Finally, the triad is constructed through the analysis of scene-feature co-occurrence matrix, which realizes the association of scenario and product features. This proposed model is of great practical value for product developers to better understand customer's requirements in specific scenarios. The experiments of scenario-feature identification from the reviews of Pacific Auto verifies the effectiveness of this method.
场景赋予产品意义。根据特定使用场景设计产品已成为赢得竞争的关键。传统的场景识别和产品特征关联方法存在主观性、成本高、粒度粗以及可识别场景有限等缺点。对此，我们提出了一种基于BERT的场景-特征识别模型，以有效地从在线评论中提取有关用户体验和使用场景的信息。首先，提出场景-特征识别框架来描述整个识别过程。然后，构建基于BERT的场景句子识别模型。使用Skip-gram和词向量相似度方法构建场景和特征词典。最后，通过对场景-特征共现矩阵的分析构建三元组，实现场景与产品特征的关联。该模型对于产品开发者更好地理解特定场景下客户的需求具有很大的实用价值。从太平洋汽车评论中进行场景-特征识别的实验验证了该方法的有效性。
