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Sentiment analysis in social internet of things using contextual representations and dilated convolution neural network

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Abstract

The methodologies based on neural networks are substantial to accomplish sentiment analysis in the Social Internet of Things (SIoT). With social media sentiment analysis, significant insights can produce efficient and intelligent applications. Neural networks such as recurrent neural networks (RNNs) and convolution neural networks (CNNs) have been considered widely in many text classification tasks. However, RNNs are computationally expensive and require complex training to capture contextual information and long-term dependencies. Similarly, traditional CNNs must stack multiple convolutional layers, requiring massive computations and additional parameters. To address these problems, this work initialized the novel architecture, in which contextual representations (CRs) based on the textual framework are proposed at the initial step. In CRs, state-of-the-art word representation models, such as GloVe (global vectors) and FastText (subword information), collectively produce word representations upon the input sequence using a weight mechanism. Secondly, a unique way is introduced: a three-parallel layered dilated convolutional network with global mean pooling. The experimental results show that the proposed methods when compared with baseline methods, the dilation in CNNs following CRs significantly increases the accuracy from 72.45 to 98.98% and reduces computational resources.

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Acknowledgements

The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work, under the Research Groups Funding program grant code (NU/RG/SERC/12/34).

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Authors and Affiliations

  1. Department of Information Systems, Dr. Hassan Murad School of Management, University of Management and Technology, Lahore, Pakistan

    Fazeel Abid

  2. Department of Computer Engineering, Istanbul Sabahattin Zaim University, 34303, Istanbul, Turkey

    Jawad Rasheed

  3. Department of Software Engineering, Istanbul Nisantasi University, 34394, Istanbul, Turkey

    Jawad Rasheed

  4. Department Computer Science, College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia

    Mohammed Hamdi & Hani Alshahrani

  5. Department Information Systems, College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia

    Mana Saleh Al Reshan & Asadullah Shaikh

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  1. Fazeel Abid
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Correspondence to Jawad Rasheed or Asadullah Shaikh.

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Abid, F., Rasheed, J., Hamdi, M. et al. Sentiment analysis in social internet of things using contextual representations and dilated convolution neural network. Neural Comput & Applic 36, 12357–12370 (2024). https://doi.org/10.1007/s00521-024-09771-2

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