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Arabic Sentiment Analysis with Noisy Deep Explainable Model

Authors:

Md. Atabuzzaman,

Maksuda Bilkis Baby,

Alexander BodenAuthors Info & Claims

NLPIR '23: Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval

Pages 185 - 189

https://doi.org/10.1145/3639233.3639241

Published: 05 March 2024 Publication History

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Abstract

Sentiment Analysis (SA) is an essential task for numerous real-world applications. However, the majority of SA research focuses on high-resource languages such as English and Chinese, while limited-resource languages like Arabic and Bengali receive less attention. Additionally, existing Arabic sentiment analysis methods based on advanced artificial intelligence (AI) approaches tend to operate as black boxes, making it challenging to comprehend the reasoning behind their predictions. This paper proposes an explainable sentiment classification framework for the Arabic language. We introduce a noise layer to different deep learning (DL) models, including BiLSTM and CNN-BiLSTM, to address the issue of overfitting. The proposed framework enables the explanation of specific predictions by training a local surrogate explainable model, shedding light on the reasons behind each sentiment prediction (positive or negative). Experiments were conducted on publicly available benchmark Arabic SA datasets, and the results demonstrated that the inclusion of noise layers in the DL model improves performance for the Arabic language by mitigating overfitting. Our method also outperformed several state-of-the-art approaches. Moreover, the introduction of explainability with the noise layer enhances transparency and accountability, making the model suitable for practical adoption in AI-enabled systems.

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Cited By

Aftan SZhuang YAseeri AShah H(2024)Steering a Standard Arab Language Processing Model Towards Accurate Saudi Dialect Sentiment Analysis Using Generative AI2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825944(5891-5900)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825944

Index Terms

Arabic Sentiment Analysis with Noisy Deep Explainable Model
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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NLPIR '23: Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval

December 2023

336 pages

ISBN:9798400709227

DOI:10.1145/3639233

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 05 March 2024

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NLPIR 2023

NLPIR 2023: 2023 7th International Conference on Natural Language Processing and Information Retrieval

December 15 - 17, 2023

Seoul, Republic of Korea

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Cited By

Aftan SZhuang YAseeri AShah H(2024)Steering a Standard Arab Language Processing Model Towards Accurate Saudi Dialect Sentiment Analysis Using Generative AI2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825944(5891-5900)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825944

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