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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • E-commerce Analytics

Background:

  • The e-commerce market is rapidly expanding, reaching US$ 13 trillion and projected to hit US$ 55.6 trillion by 2027.
  • Product reviews significantly influence consumer purchasing decisions, making manual evaluation challenging.
  • Natural Language Processing (NLP) offers automated solutions for analyzing textual data from reviews.

Purpose of the Study:

  • To analyze the Amazon dataset using deep learning and NLP techniques.
  • To develop a module for classifying review sentences into 'Positive', 'Neutral', 'Negative', or 'Indifferent' categories.
  • To evaluate the effectiveness of various Machine Learning algorithms for sentiment analysis in e-commerce.

Main Methods:

  • Analysis of the Amazon dataset incorporating voice components and deep learning.
  • Implementation of a classification module to categorize review sentiment.
  • Utilizing FastText for word embedding and a Multi-channel Convolutional Neural Network (CNN) for sentiment prediction.

Main Results:

  • The developed module successfully identifies and classifies review sentiments.
  • The system achieved a maximum validation accuracy of 79.83%.
  • FastText word embedding combined with a Multi-channel CNN proved effective for sentiment analysis.

Conclusions:

  • NLP and Machine Learning provide effective tools for automating the analysis of e-commerce reviews.
  • Accurate sentiment classification can aid in understanding consumer feedback and business models.
  • The study demonstrates the potential of deep learning models in enhancing e-commerce analytics.