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research-article

Arabic sentiment analysis using dependency-based rules and deep neural networks

Published: 01 September 2022 Publication History

Abstract

With the growth of social platforms in recent years and the rapid increase in the means of communication through these platforms, a significant amount of textual data is available that contains an abundance of individuals’ opinions. Sentiment analysis is a task that supports companies and organizations to evaluate this textual data with the intention of understanding people’s thoughts concerning services or products. Most previous research in Arabic sentiment analysis relies on word frequencies, lexicons, or black box methods to determine the sentiment of a sentence. It should be noted that these approaches do not take into account the semantic relations and dependencies between words. In this work, we propose a framework that incorporates Arabic dependency-based rules and deep learning models. Dependency-based rules are created by using linguistic patterns to map the meaning of words to concepts in the dependency structure of a sentence. By examining the dependent words in a sentence, the general sentiment is revealed. In the first stage of sentiment classification, the dependency grammar rules are used. If the rules are unsuccessful in classifying the sentiment, the algorithm then applies deep neural networks (DNNs). Three DNN models were employed, namely LSTM, BiLSTM, and CNN, and several Arabic benchmark datasets were used for sentiment analysis. The performance results of the proposed framework show a greater improvement in terms of accuracy and F1 score and they outperform the state-of-the-art approaches in Arabic sentiment analysis.

Highlights

Innovative dependency rule-based approach for Arabic sentiment analysis. These rules overcome the limitation of word frequency based approaches by employing linguistic patterns that permit the sentiment to transfer from words to concepts based on the dependency structure of a sentence. Furthermore, these rules are fully explainable and explore the terms and dependencies more comprehensively to provide a justification for each production. Thus, understanding the model predictions in an interpretable way can provide trust and transparency.
A comparative analysis of the proposed hybrid Arabic Sentiment Analysis Framework with Logistic Regression, Support Vector Machine, Convolutional Neural Network, Long–Short Term Memory and Bidirectional LSTM.
An ablation study of the proposed dependency rule-based approach on several datasets illustrating the importance of each rule.
Overcoming the limitation of unclassified reviews using Arabic dependency rule-based approach by combining the rules with DNN models.

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  • (2024)Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment AnalysisIEEE Transactions on Affective Computing10.1109/TAFFC.2023.329637315:3(837-846)Online publication date: 1-Jul-2024
  • (2024)Fractional-order Q-learning based on modal decomposition and convolutional neural networks for voltage control of smart gridsApplied Soft Computing10.1016/j.asoc.2024.111825162:COnline publication date: 1-Sep-2024
  • (2024)An aspect-opinion joint extraction model for target-oriented opinion words extraction on global spaceApplied Intelligence10.1007/s10489-024-05865-555:1Online publication date: 25-Nov-2024

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            cover image Applied Soft Computing
            Applied Soft Computing  Volume 127, Issue C
            Sep 2022
            721 pages

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            Elsevier Science Publishers B. V.

            Netherlands

            Publication History

            Published: 01 September 2022

            Author Tags

            1. Arabic sentiment analysis
            2. Natural language processing
            3. Deep learning
            4. Dependency-based rules

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            View all
            • (2024)Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment AnalysisIEEE Transactions on Affective Computing10.1109/TAFFC.2023.329637315:3(837-846)Online publication date: 1-Jul-2024
            • (2024)Fractional-order Q-learning based on modal decomposition and convolutional neural networks for voltage control of smart gridsApplied Soft Computing10.1016/j.asoc.2024.111825162:COnline publication date: 1-Sep-2024
            • (2024)An aspect-opinion joint extraction model for target-oriented opinion words extraction on global spaceApplied Intelligence10.1007/s10489-024-05865-555:1Online publication date: 25-Nov-2024

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