A Bi-Directional GRU Architecture for the Self-Attention Mechanism: An Adaptable, Multi-Layered Approach with Blend of Word Embedding


  • Amit Pimpalkar School of Computing, Sathyabama Institute of Science and Technology, Chennai, India; Department of Computer Science and Engineering, Jhulelal Institute of Technology, Nagpur, India
  • Jeberson Retna Raj School of Computing, Sathyabama Institute of Science and Technology, Chennai, India




Bi-directional GRU, attention mechanism, deep learning, natural language processing, word embedding


Sentiment analysis (SA) has become an essential component of natural language processing (NLP) with numerous practical applications to understanding “what other people think”. Various techniques have been developed to tackle SA using deep learning (DL); however, current research lacks comprehensive strategies incorporating multiple-word embeddings. This study proposes a self-attention mechanism that leverages DL and involves the contextual integration of word embedding with a time-dispersed bidirectional gated recurrent unit (Bi-GRU). This work employs word embedding approaches GloVe, word2vec, and fastText to achieve better predictive capabilities. By integrating these techniques, the study aims to improve the classifier’s capability to precisely analyze and categorize sentiments in textual data from the domain of movies. The investigation seeks to enhance the classifier’s performance in NLP tasks by addressing the challenges of underfitting and overfitting in DL. To evaluate the model’s effectiveness, an openly available IMDb dataset was utilized, achieving a remarkable testing accuracy of 99.70%.


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How to Cite

Amit Pimpalkar and Jeberson Retna Raj, “A Bi-Directional GRU Architecture for the Self-Attention Mechanism: An Adaptable, Multi-Layered Approach with Blend of Word Embedding”, Int. j. eng. technol. innov., vol. 13, no. 3, pp. 251–264, Jul. 2023.