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With increasing online customers, it is becoming harder for businesses to remain competitive and retain customers. To help address this problem this study focuses on the extraction of customer trust, loyalty and retention from online customer reviews to better understand customer feelings through sentiment analysis. A Long Short-Term Memory (LSTM) model was developed with a 93% accuracy and the model significance, and its limitations discussed before proposing future enhancements.

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An Aspect-Based Sentiment Analysis Model for Extracting Customer Trust, Loyalty and Retention for E-commerce

  • 章节

  • 发表日期：

    2025年

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  • Veerajay Gooljar,
  • Tomayess Issa,
  • Sarita Hardin‑Ramanan,
  • Bilal Abu‑Salih

摘要

With increasing online customers, it is becoming harder for businesses to remain competitive and retain customers. To help address this problem this study focuses on the extraction of customer trust, loyalty and retention from online customer reviews to better understand customer feelings through sentiment analysis. A Long Short-Term Memory (LSTM) model was developed with a 93% accuracy and the model significance, and its limitations discussed before proposing future enhancements.