Explainable artificial intelligence approach towards classifying educational android app reviews using deep learning

Zahoor, Kanwal; Bawany, Narmeen Zakaria

doi:10.1080/10494820.2023.2212708

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2024

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Cited by 4 publications

References 41 publications

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Towards Trustworthy and Explainable AI Educational Systems

Shafik

2024

Information Systems Engineering and Management

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Towards Trustworthy and Explainable AI Educational Systems

Shafik

2024

Information Systems Engineering and Management

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Interpretable App Review Classification with Transformers

Biswas,

Anish,

Ghaisas

2024

2024 IEEE 32nd International Requirements Engineering Conference Workshops (REW)

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An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster Tweets

Mu,

Li,

et al. 2024

Biomimetics

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The Internet’s development has prompted social media to become an essential channel for disseminating disaster-related information. Increasing the accuracy of emotional polarity recognition in tweets is conducive to the government or rescue organizations understanding the public’s demands and responding appropriately. Existing sentiment analysis models have some limitations of applicability. Therefore, this research proposes an IDBO-CNN-BiLSTM model combining the swarm intelligence optimization algorithm and deep learning methods. First, the Dung Beetle Optimization (DBO) algorithm is improved by adopting the Latin hypercube sampling, integrating the Osprey Optimization Algorithm (OOA), and introducing an adaptive Gaussian–Cauchy mixture mutation disturbance. The improved DBO (IDBO) algorithm is then utilized to optimize the Convolutional Neural Network—Bidirectional Long Short-Term Memory (CNN-BiLSTM) model’s hyperparameters. Finally, the IDBO-CNN-BiLSTM model is constructed to classify the emotional tendencies of tweets associated with the Hurricane Harvey event. The empirical analysis indicates that the proposed model achieves an accuracy of 0.8033, outperforming other single and hybrid models. In contrast with the GWO, WOA, and DBO algorithms, the accuracy is enhanced by 2.89%, 2.82%, and 2.72%, respectively. This study proves that the IDBO-CNN-BiLSTM model can be applied to assist emergency decision-making in natural disasters.

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Explainable artificial intelligence approach towards classifying educational android app reviews using deep learning

Cited by 4 publications

References 41 publications

Towards Trustworthy and Explainable AI Educational Systems

Towards Trustworthy and Explainable AI Educational Systems

Interpretable App Review Classification with Transformers

An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster Tweets

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