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Efficient Long Short-Term Memory-Based Sentiment Analysis of E-Commerce Reviews.

Naveen Kumar Gondhi1, Chaahat1, Eishita Sharma1

  • 1Shri Mata Vaishno Devi University, Katra, Jammu & Kashmir, India.

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This summary is machine-generated.

Sentiment analysis of e-commerce reviews using Long Short-Term Memory (LSTM) and word2vec improves product understanding. This sentiment analysis approach enhances online shopping experiences for consumers and manufacturers.

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Area of Science:

  • Computer Science
  • Natural Language Processing

Background:

  • E-commerce growth increases reliance on online reviews for purchasing decisions.
  • Sentiment analysis is crucial for understanding customer feedback in e-commerce.
  • Accurate review analysis benefits both consumers and manufacturers.

Purpose of the Study:

  • To describe the process and requirements of sentiment analysis for e-commerce reviews.
  • To enhance the performance of sentiment analysis models for product reviews.

Main Methods:

  • Utilized the Amazon Review dataset 2018 for research.
  • Implemented Long Short-Term Memory (LSTM) networks combined with word2vec embeddings.
  • Employed a gating mechanism within the LSTM during training.

Main Results:

  • The proposed LSTM model demonstrated improved performance in sentiment analysis.
  • Achieved higher results on accuracy, precision, recall, and F1 score compared to baseline models.
  • The combination of LSTM and word2vec enhanced sentiment analysis effectiveness.

Conclusions:

  • The developed LSTM model with word2vec is effective for e-commerce sentiment analysis.
  • This approach offers valuable insights from customer reviews.
  • Optimized sentiment analysis can significantly aid online retail platforms.