Скачать или смотреть Aspect-based sentiment-analysis using topic modelling and machine-learning

Authors: Radhika Jinendra Dhanal, Vijay Ram Ghorpade (IJECE ID 36104)
This study addresses the critical need for an accurate aspect-based sentiment analysis (ABSA) model to understand sentiments effectively. The existing ABSA models often face challenges in accurately extracting aspects and determining sentiment polarity from textual data. As a result, we propose a novel approach that leverages latent-Dirichlet-allocation (LDA) for aspect extraction and transformer-based bidirectional encoder representations from transformers (TF-BERT) for sentiment-polarity evaluation. We conducted the experiments on the SemEval 2014 laptop and restaurant datasets. We also generated a multi-domain dataset by combining SemEval 2014, Amazon, and hospital reviews. The results demonstrate the superiority of the LDA-TF-BERT model, with 82.19% accuracy and 79.52% macro-F1 score for the laptop task and 86.26% accuracy of 87.26% and 81.27% for the restaurant task. This showcases the model's robustness and effectiveness in accurately analyzing textual data and extracting meaningful insights. The novelty of our work lies in combining LDA and TF-BERT, providing a comprehensive and accurate ABSA solution for various industries, thereby contributing significantly to the advancement of sentiment analysis techniques.

International Journal of Electrical and Computer Engineering (IJECE)
https://ijece.iaescore.com

Supported by Master Program of Electrical and Computer Engineering, Universitas Ahmad Dahlan, https://mee.uad.ac.id #yogyakarta
Admission: https://mee.uad.ac.id/pendaftaran/

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