Hybrid Deep Neural Network with Domain Knowledge for Text Sentiment Analysis

Jawad Khan, Niaz Ahmad, Youngmoon Lee (), Shah Khalid and Dildar Hussain ()
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Jawad Khan: School of Computing, Gachon University, Seongnam 13120, Republic of Korea
Niaz Ahmad: Department of Computer Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
Youngmoon Lee: Department of Robotics, Hanyang University, Ansan 15588, Republic of Korea
Shah Khalid: School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan
Dildar Hussain: Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea

Mathematics, 2025, vol. 13, issue 9, 1-25

Abstract: Sentiment analysis (SA) analyzes online data to uncover insights for better decision-making. Conventional text SA techniques are effective and easy to understand but encounter difficulties when handling sparse data. Deep Neural Networks (DNNs) excel in handling data sparsity but face challenges with high-dimensional, noisy data. Incorporating rich domain semantic and sentiment knowledge is crucial for advancing sentiment analysis. To address these challenges, we propose an innovative hybrid sentiment analysis approach that combines established DNN models like RoBERTA and BiGRU with an attention mechanism, alongside traditional feature engineering and dimensionality reduction through PCA. This leverages the strengths of both techniques: DNNs handle complex semantics and dynamic features, while conventional methods shine in interpretability and efficient sentiment extraction. This complementary combination fosters a robust and accurate sentiment analysis model. Our model is evaluated on four widely used real-world benchmark text sentiment analysis datasets: MR, CR, IMDB, and SemEval 2013. The proposed hybrid model achieved impressive results on these datasets. These findings highlight the effectiveness of this approach for text sentiment analysis tasks, demonstrating its ability to improve sentiment analysis performance compared to previously proposed methods.

Keywords: sentiment analysis; domain knowledge; dimensionality reduction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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