User Satisfaction in the New Energy Vehicles: An Analysis Harnessing User-Generated Content and Sentiment Analytics

Zhaoguang Xu, Shumeng Gui, Yanzhong Dang, Xianneng Li

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

Abstract

Automotive forums offer abundant and genuine data from car enthusiasts, enabling larger-scale customer satisfaction research. Current studies utilizing forum data for customer satisfaction measurement tend to be coarse-grained due to the intricacy of automotive products. As such, we introduce an improved aspect attention-based BiLSTM with aspect embedding (AATAE_BiLSTM) model, precisely identifying users' aspect-level sentiment tendencies. Employing a hierarchical indicator system, we capture users' sentiment tendencies at three levels, from fine-grained to coarse-grained. Moreover, we present a novel comprehensive quantitative model of satisfaction and attention based on sentiment tendencies, facilitating the measurement of users' satisfaction and attention across different indicator levels. We analyze BYD's NEV word-of-mouth comments data from AutoHome to gauge user satisfaction with BYD NEVs. Our study offers valuable insights for NEV companies to enhance their products and increase competitiveness.

Original languageEnglish
Pages (from-to)1242-1249
Number of pages8
JournalProcedia Computer Science
Volume221
DOIs
StatePublished - 2023
Externally publishedYes
Event10th International Conference on Information Technology and Quantitative Management, ITQM 2023 - Oxfordshire, United Kingdom
Duration: 12 Aug 202314 Aug 2023

Keywords

  • aspect-level sentiment analysis
  • customer satisfaction
  • new energy vehicle
  • user-generated content

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