A Deep Learning-Fuzzy based hybrid ensemble approach for Aspect level sentiment classification

Aspect level sentiment classification (ALSC) has gained high importance in the era of an e-commerce-based economy. It allows manufacturers to improve the designs of their products based on users' feedback. However, only a few datasets of limited domains are available for ALSC task. To push forw...

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Bibliographic Details
Published in:Informatica (Ljubljana) 2023-05, Vol.47 (6), p.115-129
Main Authors: Sharma, Tanu, Kaur, Kamaldeep
Format: Article
Language:English
Subjects:
Artificial neural networks
Automation
Classification
Datasets
Domains
Machine learning
Mathematical analysis
Neural networks
Sentiment analysis
Online Access:Get full text
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Summary:Aspect level sentiment classification (ALSC) has gained high importance in the era of an e-commerce-based economy. It allows manufacturers to improve the designs of their products based on users' feedback. However, only a few datasets of limited domains are available for ALSC task. To push forward the research in automated ALSC, this study contributes car dataset of the automobile domain. In this study, a novel fuzzy ensemble technique is also proposed based on the mathematical analysis of confidence scores of base deep neural networks. The proposed approach allows for the correction of the misclassifications of base deep learners through a reward and penalization strategy. The experimental results on five benchmark datasets show that the proposed approach outperforms the constituent base deep neural networks and several other important baselines. The proposed Fuzzy ensemble also performed at par with the most recent Graph Convolution Neural Networks on the basis of Friedman and Nemenyi Tests.
ISSN:0350-5596
1854-3871
DOI:10.31449/inf.v47i6.4607