Aspect-based Sentiment Analysis with Ontology-assisted Recommender System on Multilingual Data using Optimised Self-attention and Adaptive Deep Learning Network

Archana Nagelli () and B. Saleena
Additional contact information
Archana Nagelli: School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, India
B. Saleena: School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, India

Journal of Information & Knowledge Management (JIKM), 2025, vol. 24, issue 03, 1-44

Abstract: In recent times of application, the Natural Language Processing (NLP) and Aspect-Based Sentiment Analysis (ABSA) seek to forecast the sentiment of polarity in several components of a document or sentence. Much present research concentrates on the relationship between aspect local context and sentiment polarity. There wasn’t enough focus on the significant deep relationships between the aspect sentiment and global context polarity. Some scholars have concluded that supervised algorithms provide promising results for ABSA. However, individually labelling information to train unsupervised systems in various domains and languages is time-consuming and expensive. Therefore, for multilingual reviews, a new ABSA model with ontology for recommendations is developed in this study. The text reviews are initially gathered from traditional online sources and then preprocessed to improve text data quality. For instance, the preprocessed data is subjected to the aspect extraction process. Then, these extracted aspects are given to the self-attention and adaptive model named SATANet for ABSA, where the guided transformer network with Dilated Deep Convolutional Network (DDCN) is used to classify the sentiments. In this SATANet, the network variables are optimised with the help of the suggested Random Position of Bonobo and Reptile Search Algorithm (RP-BRSA) to improve the recommendation performance. The final recommendation is implemented using ontology-based models, and the experimental results are validated through various heuristic algorithms and previous sentiment analysis models by considering various performance metrics.

Keywords: Ontology-based recommender system; aspect-based sentiment analysis; guided transformer network with dilated deep convolutional networks; random position of bonobo and reptile search algorithm (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219649225500224
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:jikmxx:v:24:y:2025:i:03:n:s0219649225500224

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219649225500224

Access Statistics for this article

Journal of Information & Knowledge Management (JIKM) is currently edited by Professor Suliman Hawamdeh

More articles in Journal of Information & Knowledge Management (JIKM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().