Class-decomposition and augmentation for imbalanced data sentiment analysis.

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

Abstract

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.

Citation

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: https://doi.org/10.1109/IJCNN52387.2021.9533603

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2021 International joint conference on neural networks (IJCNN 2021)
• 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)
• Peer Reviewed: Peer Reviewed
• Series ISSN: 2161-4407
• Book Title: Proceedings of 2021 Internationa joint confernce on neural networks (IJCNN 2021)
• ISBN: 9780738133669
• DOI: https://doi.org/10.1109/ijcnn52387.2021.9533603
• Keywords: Sentiment analysis; Text imbalanced datasets; Class decomposition
• Public URL: https://rgu-repository.worktribe.com/output/1465455