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Optimal deep recurrent neural network for sentiment grade classification

Sireesha Jasti (VNRVJIET Computer Science and Engineering, Gandhi Institute of Technology and Management, Visakhapatnam, India)

International Journal of Web Information Systems

ISSN: 1744-0084

Article publication date: 16 September 2021

Issue publication date: 1 December 2021

99

Abstract

Purpose

Internet has endorsed a tremendous change with the advancement of the new technologies. The change has made the users of the internet to make comments regarding the service or product. The Sentiment classification is the process of analyzing the reviews for helping the user to decide whether to purchase the product or not.

Design/methodology/approach

A rider feedback artificial tree optimization-enabled deep recurrent neural networks (RFATO-enabled deep RNN) is developed for the effective classification of sentiments into various grades. The proposed RFATO algorithm is modeled by integrating the feedback artificial tree (FAT) algorithm in the rider optimization algorithm (ROA), which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of term frequency-inverse document frequency (TF-IDF) features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted. The metrics employed for the evaluation in the proposed RFATO algorithm are accuracy, sensitivity, and specificity.

Findings

By using the proposed RFATO algorithm, the evaluation metrics such as accuracy, sensitivity and specificity are maximized when compared to the existing algorithms.

Originality/value

The proposed RFATO algorithm is modeled by integrating the FAT algorithm in the ROA, which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of TF-IDF features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted.

Keywords

Citation

Jasti, S. (2021), "Optimal deep recurrent neural network for sentiment grade classification", International Journal of Web Information Systems, Vol. 17 No. 6, pp. 557-579. https://doi.org/10.1108/IJWIS-12-2020-0075

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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