A Bi-LSTM and GRU Hybrid Neural Network with BERT Feature Extraction for Amazon Textual Review Analysis

A Bi-LSTM and GRU Hybrid Neural Network with BERT Feature Extraction for Amazon Textual Review Analysis

  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-5
Year of Publication : 2022
Authors : Madhuri V. Joseph
DOI :  10.14445/22315381/IJETT-V70I5P216

How to Cite?

Madhuri V. Joseph, "Optical and Electrochromic Properties of CeO2/WO3 Hybrid Thin Films Prepared by Hydrothermal and Sputtering," International Journal of Engineering Trends and Technology, vol. 70, no. 5, pp. 1-8, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P216

Abstract
Nowadays, businesses move towards digital platforms for their product promotion and to improve their overall profit margin. Customer reviews determine the purchase decision of the specified products in the e-commerce system in this digital world. In this case, reviewing products before buying is the common scenario in this current world. It will help the buyers to buy quality products at affordable prices. On this basis, it is necessary to implement deep learning techniques to analyze the sentimental tweets of customers based on their product ratings. The study planned to propose an enhanced BERT algorithm in feature extraction on sentiment analysis with a large data set and hybridized deep Bi-LSTM-GRU neural network for the classification process.

Consequently, amazon products` customer review datasets are employed for the analysis. The review about mobile phones is retrieved from amazon for the sentiment analysis. Initially, the data was preprocessed to increase the accuracy and performance of the classifier. Further, the feature extraction is done to reduce many data into accurate ones with BERT (Bidirectional Encoder Representations from Transformers) algorithm. It isn`t easy to evaluate the review sentiments without efficient classification. After this process, the study analyses the performance of the deep Bi-LSTM-GRU neural network method with the existing method. Finally, the study concluded that the proposed algorithm achieved 97.87, 98.36, 98.89, and 98.47, respectively, based on the accuracy level, frequency, precision, and recall measures. These performance measures are higher than the existing algorithms developed in the studies. The study achieved more accuracy and efficiency through the proposed deep Bi-LSTM-GRU neural network method in sentiment analysis on mobile phone reviews in the amazon e-commerce system.

Keywords
Sentiment analysis, Feature extraction, BERT, Bi-LSTM, GRU neural network.

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