Fine-Tuned Deep Contextual BERT for Enhanced Aspect-based Sentiment Analysis: A Comparative Study on Laptop Reviews

Title

Fine-Tuned Deep Contextual BERT for Enhanced Aspect-based Sentiment Analysis: A Comparative Study on Laptop Reviews

Creator

Rajan, Abraham; Manur, Manohar

Description

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.

Source

Proceedings of the 7th International Conference on Intelligent Sustainable Systems, ICISS 2025;pp.938-943

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Aspect based sentiment analysis; BERT model; Fine-Tuned Deep Contextual-BERT (FTDC-BERT); Natural Language Processing; Sentiment polarity

Coverage

Rajan A., Christ (Deemed to be University), Department of Cse, Bangalore, India; Manur M., Christ (Deemed to be University), Department of Cse, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833152243-8;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICISS63372.2025.11076195

https://www.scopus.com/pages/publications/105012219887?origin=resultslist