Sentiment Analysis Of E-Commerce Product Reviews Using An Attnetion Based Deep Learning Model

Authors

  • Sathya. P
  • Anuratha .V
  • Elamparithi .M

Keywords:

Sentiment Analysis, E-commerce product reviews, Amazon dataset, Deep Learning (DL), Data Preprocessing, Word Embedding, and Sentiment Classification

Abstract

Mawkishness study of a big amount of operator appraisals on e-commerce podiums can efficiently advance user consummation. Numerous methods have been future to get understandings from these statistics. Here are still tests in dealing with the text of huge size; precise sen5timent analysis of E-commerce product appraisals is an ongoing and thrilling problematic. This paper suggests a sentimentality analysis of E-commerce creation reviews by by novel deep knowledge model. The proposed system mainly involves three phase such as data preprocessing, word embedding, and sentiment classification. To begin, the collected data from Amazon review 2018 dataset is preprocessed to improve the quality of data. After that, the word embedding is performed by using Period Occurrence Converse Text Incidence and Glove methods. Finally, the sentiment classification is done by hybrid Residual Network 50-Gated Recurrent Unit with Self attention (HRN50-GRUSA) that classifies the review as positive or negative. The experiential fallouts designate that the future cross profound education with courtesy construction outdoes the conservative approaches in footings of correctness, drumming, memory, and damage metrics.

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References

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Published

2025-06-21

How to Cite

1.
P S, .V A, .M E. Sentiment Analysis Of E-Commerce Product Reviews Using An Attnetion Based Deep Learning Model. J Neonatal Surg [Internet]. 2025Jun.21 [cited 2025Jul.19];14(32S):1507-15. Available from: https://jneonatalsurg.com/index.php/jns/article/view/7602

Issue

Vol. 14 No. 32S (2025): Journal of Neonatal Surgery

Section

Original Article

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