New information search model for online reviews with the perspective of user requirements

Cheng Hsiung Weng, Cheng Kui Huang, Yen Liang Chen, Yu Shan Huang

Research output: Contribution to journalArticlepeer-review


Many e-commerce websites currently provide online reviews to share e-shoppers’ experience with the products. To help e-shoppers obtaining information efficiently, these websites usually summarize product information based on their certain predefined aspects. However, e-shopper’s aspects should be annotated to make sure that more highly related information of online reviews can be fetched for fulfilling e-shopper’s requirements. Hence, this study integrates an annotation approach with similarity techniques (Keyword pair similarity and Aspect-sentence similarity) to propose a new framework to fetch more highly correlated sentences for e-shoppers. Experimental results show that most of the combinations in the proposed approach have high prediction performance in the Top 10 sentences with Precision (0.90 or higher).

Original languageEnglish
Pages (from-to)28165-28185
Number of pages21
JournalMultimedia Tools and Applications
Issue number18
StatePublished - Jul 2023


  • E-commerce
  • Google distance
  • Keyword annotation
  • Online review
  • WordNet


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