Using Natural Language Processing to Enhance Customer Sentiment Analysis in Ecommerce
DOI:
https://doi.org/10.62951/ijies.v1i2.90Keywords:
Natural Language Processing, sentiment analysis, ecommerce, customer experience, BERT, transformer modelsAbstract
Customer sentiment analysis provides valuable insights for ecommerce businesses, but traditional methods often fall short in handling complex and contextrich language. This paper explores the use of Natural Language Processing (NLP) techniques, including BERT and transformer models, to improve sentiment analysis accuracy in ecommerce. The study compares the performance of different NLP models in capturing nuanced customer sentiment from online reviews. Findings indicate that advanced NLP techniques substantially increase accuracy and offer practical applications for improving customer experience and business strategy in ecommerce.
Keywords: Natural Language Processing, sentiment analysis, ecommerce, customer experience, BERT, transformer models.
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