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Class-decomposition and augmentation for imbalanced data sentiment analysis.

Moreno-Garcia, Carlos Francisco; Jayne, Chrisina; Elyan, Eyad


Chrisina Jayne


Significant progress has been made in the area of text classification and natural language processing. However, like many other datasets from across different domains, text-based datasets may suffer from class-imbalance. This problem leads to model's bias toward the majority class instances. In this paper, we present a new approach to handle class-imbalance in text data by means of unsupervised learning algorithms. We present class-decomposition using two different unsupervised methods, namely k-means and Density-Based Spatial Clustering of Applications with Noise, applied to two different sentiment analysis data sets. The experimental results show that utilizing clustering to find within-class similarities can lead to significant improvement in learning algorithm's performances as well as reducing the dominance of the majority class instances without causing information loss.


MORENO-GARCIA, C.F., JAYNE, C. and ELYAN, E. 2021. Class-decomposition and augmentation for imbalanced data sentiment analysis. In Proceedings of 2021 International joint conference on neural networks (IJCNN 2021), 18-22 July 2021, [virtual conference]. Piscataway: IEEE [online], article 9533603. Available from:

Conference Name 2021 International joint conference on neural networks (IJCNN 2021)
Conference Location [virtual conference]
Start Date Jul 18, 2021
End Date Jul 22, 2021
Acceptance Date Apr 10, 2021
Online Publication Date Jul 22, 2021
Publication Date Sep 20, 2021
Deposit Date Sep 24, 2021
Publicly Available Date Sep 24, 2021
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Series ISSN 2161-4407
Book Title Proceedings of 2021 Internationa joint confernce on neural networks (IJCNN 2021)
ISBN 9780738133669
Keywords Sentiment analysis; Text imbalanced datasets; Class decomposition
Public URL


MORENO-GARCIA 2021 Class-decomposition (AAM) (441 Kb)

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