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Predicting customer sentiment: the fusion of deep learning and a fuzzy system for sentiment analysis of Arabic text

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Abstract

Understanding client feedback and satisfaction is a critical concern for any business organization operating in the highly competitive internet industry. Notably, social media platforms such as X (Twitter) act as forums for customers to voice their opinions. Analyzing such feedback is beneficial since it provides insights into client interests. The proposed model addresses various challenges, such as measuring customer satisfaction levels from Arabic text by proposing a hybrid deep learning technique enriched with fuzzy logic. The proposed system aims to construct an Arabic sentiment-based system that uses an innovative combination of fuzzy logic and a deep neural network to evaluate customer satisfaction, hence assisting businesses in improving their service and product quality. To forecast sentiment polarity (positive or negative), the proposed method employs bidirectional long short-term memory (LSTM) with an attention component. Following that, the level of consumer contentment is determined using fuzzy logic. Ablation studies demonstrate the importance of the attention mechanism, which contributes to a considerable improvement in accuracy compared to a BiLSTM-only model. Fuzzy logic incorporation increases the ability of a model to handle imprecision and uncertainty in sentiment formulations, helping it to additionally correct sentiment analysis. Furthermore, hyperparameter adjustment improves performance by highlighting the model's sensitivity to specific variables. The system achieved an excellent accuracy of 95%, outperforming earlier baseline techniques. Furthermore, the efficacy of the suggested approach was demonstrated using statistical testing.

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Data availability

No datasets were generated or analysed during the current study.

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Funding

No specific funding was received for this work.

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Authors and Affiliations

  1. Gomal Research Institute of Computing (GRIC), Faculty of Computing, Gomal University, D.I. Khan, 29220, Pakistan

    Shela Ambreen, Muhammad Iqbal & Muhammad Zubair Asghar

  2. Department of Computer Science, Virtual Université of Pakistan, Lahore, 51000, Pakistan

    Tehseen Mazhar

  3. School of Information Technology, UNITAR International University, Kelana Jaya, 47301, Petaling Jaya, Malaysia

    Umar Farooq Khattak

  4. School of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia

    Muhammad Amir Khan

  5. Faculty of Engineering, Uni de Moncton, Moncton, NB, E1A3E9, Canada

    Habib Hamam

  6. Hodmas University College, Taleh Area, Mogadishu, Somalia

    Habib Hamam

  7. Bridges for Academic Excellence, Tunis, Tunisia

    Habib Hamam

  8. Department of Electrical and Electronic English Science, School of Electrical Engineering, University of Johannesburg, Johannesburg, 2006, South Africa

    Habib Hamam

Authors
  1. Shela Ambreen
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  2. Muhammad Iqbal
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  3. Muhammad Zubair Asghar
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  4. Tehseen Mazhar
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  5. Umar Farooq Khattak
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  6. Muhammad Amir Khan
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  7. Habib Hamam
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All authors have equally contributed.

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Correspondence to Tehseen Mazhar or Muhammad Amir Khan.

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Ambreen, S., Iqbal, M., Asghar, M.Z. et al. Predicting customer sentiment: the fusion of deep learning and a fuzzy system for sentiment analysis of Arabic text. Soc. Netw. Anal. Min. 14, 206 (2024). https://doi.org/10.1007/s13278-024-01356-0

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  • DOI: https://doi.org/10.1007/s13278-024-01356-0

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