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Scenario-feature identification from online reviews based on BERT.

Xunjiang Huang1, Kang Yan1

  • 1School of Business Administration, Northeastern University, Shenyang, Liaoning Province, China.

Peerj. Computer Science
|June 22, 2023
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Summary

This study introduces a BERT-based model to automatically identify product usage scenarios and features from online reviews. This method overcomes limitations of traditional approaches, aiding product development by understanding customer needs in specific contexts.

Keywords:
BERTCo-occurrence matrixOnline reviewsScenario identification

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Product Development

Background:

  • Product design increasingly relies on understanding specific usage scenarios.
  • Traditional methods for scenario identification are subjective, costly, and lack granularity.
  • Effective scenario-feature association is crucial for competitive product development.

Purpose of the Study:

  • To propose a novel BERT-based model for automated scenario-feature identification from online user reviews.
  • To overcome the limitations of existing methods in terms of subjectivity, cost, and granularity.
  • To provide product developers with a tool for better understanding customer requirements in specific usage contexts.

Main Methods:

  • Development of a BERT-based scenario-feature identification framework.
  • Construction of a BERT-based scene-sentence recognition model.
  • Utilizing Skip-gram and word vector similarity for scene and feature lexicon creation.
  • Analysis of a scene-feature co-occurrence matrix to associate scenarios with product features.

Main Results:

  • The proposed model effectively extracts user experience and usage scene information from online reviews.
  • The method demonstrates practical value in associating product features with specific scenarios.
  • Experimental validation on Pacific Auto reviews confirms the model's effectiveness.

Conclusions:

  • The BERT-based model offers an objective and granular approach to scenario-feature identification.
  • This approach significantly enhances the understanding of customer needs within specific product usage contexts.
  • The findings have direct implications for improving product development strategies and customer satisfaction.