Exploring Multi-Level User Perceived Quality through Dependency Syntax Analysis and Hierarchical Clustering

Shumeng Gui, Zhaoguang Xu, Yanzhong Dang

Research output: Contribution to journalConference articlepeer-review

Abstract

The vast and unstructured data on social media platforms offer insights into user-perceived quality, presenting a novel avenue for new energy vehicle companies to analyze product quality. In response, this study introduces a methodology that integrates dependency syntactic parsing with hierarchical clustering to derive multi-level insights on user-perceived quality. Initially, we employ dependency syntactic parsing and part-of-speech tagging to identify compound noun phrases within comments. These phrases serve as a specialized out-of-vocabulary library pertinent to the new energy vehicle sector, from which a selection of words is chosen as potential evaluation metrics. Subsequently, we utilize Word2Vec to develop word vectors from automotive forum corpora. Leveraging these word vectors and the identified evaluation metrics, a hierarchical clustering algorithm is then applied to establish a comprehensive three-level user-perceived quality indicator system. Experimental validation conducted on forum comment data from BYD's new energy vehicles confirms the reliability and effectiveness of the proposed methodology. The user-perceived quality extraction method delineated in this research aids automotive firms in pinpointing areas of user interest, thereby substantially enhancing user loyalty and satisfaction.

Original languageEnglish
Pages (from-to)576-583
Number of pages8
JournalProcedia Computer Science
Volume242
DOIs
StatePublished - 2024
Externally publishedYes
Event11th International Conference on Information Technology and Quantitative Management, ITQM 2024 - Bucharest, Romania
Duration: 23 Aug 202425 Aug 2024

Keywords

  • attribute extraction
  • automotive industry
  • dependency parsing
  • hierarchical clustering
  • perceived quality

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