Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Rajan, Abraham; Manur, Manohar Title A name given to the resource Fine-Tuned Deep Contextual BERT for Enhanced Aspect-based Sentiment Analysis: A Comparative Study on Laptop Reviews Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the 7th International Conference on Intelligent Sustainable Systems, ICISS 2025;pp.938-943 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICISS63372.2025.11076195" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICISS63372.2025.11076195</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105012219887?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012219887?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Rajan A., Christ (Deemed to be University), Department of Cse, Bangalore, India; Manur M., Christ (Deemed to be University), Department of Cse, Bangalore, India Description An account of the resource Sentiment analysis entails the care full analysis, conduction of interpretation and conclusion of subjective texts even as an evaluation. In the business context, the companies' strategies towards growth makes use of both level of experience of consumers, market reach, social media, opinion and reputation of the brand. The different levels of performing the analysis includes the analysis at the document, phrase, and aspect levels. The sentiment which targets the polarity on some components of texts is often recognized by various Natural language processing (NLP) tasks for example aspect level sentiment analysis. This study presents the fine-tuned deep contextual BERT (FTDC BERT) aiming at improving the accuracy of sentiment polarization prediction. We look at different types of models including the LSTM based and the attention based and the BERT based models and where they performed on the laptop dataset. The fine-tuned and pre-trained BERT model exceeded all benchmarks and gave the most accurate work at 84.48%. This remarkable achievement testifies to the capability of the model in adapting its structure to varying degrees of sentiment contained in laptop reviews. Based on the comparative analysis, different models have different degree of success which indicates that sentiment has to be modelled separately for every set of data. This paper describes interesting areas of the future inline sentiment analysis for researchers and practitioners. 2025 IEEE. Subject The topic of the resource Aspect based sentiment analysis; BERT model; Fine-Tuned Deep Contextual-BERT (FTDC-BERT); Natural Language Processing; Sentiment polarity Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152243-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online