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All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Rajpurohit, Vijay
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Computer Science & IT

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Hybrid deep neural network model for aspect and opinion extraction with multi-head attention-driven sentiment analysis Shirahatti, Abhinandan; Medar, Ramesh; Rajpurohit, Vijay; Kaulgud, Sanjeev; Mathad Shivamurthaiah, Mrutyunjaya
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp769-777

Abstract

Finding and extracting significant features from review sentences is known as aspect triplet extraction, and it provides succinct information on the elements that users have addressed. This method makes sentiment analysis and opinion mining easier, which helps to provide an adequate understanding of user opinions in reviews. This research presents a novel approach to achieve aspect-sentiment triplet-extraction (ASTE) using a deep neural network and transformer-based multi-head attention model. The proposed hybrid model adopts a pipeline methodology, concurrently extracting opinions and aspects while performing sentiment classification. The study addresses the intricate challenge of identifying triplets that capture nuanced relationships between terms and sentences, employing a deep neural network for joint extraction of aspects and opinions using a sequential tagging method. Sentiment classification is seamlessly integrated into the pipeline, treating sentiment recognition as a classification task, and aspect and opinion extraction as text-extraction challenges. Evaluations was out experimentally on the SemEval 2016 restaurant dataset demonstrate the effectiveness of the model, despite issues with unequal distribution of data.
Co-Authors Kaulgud, Sanjeev Mathad Shivamurthaiah, Mrutyunjaya Medar, Ramesh Shirahatti, Abhinandan