Class-Decomposition and Augmentation for Imbalanced Data Sentiment Analysis

Carlos Francisco Moreno-Garcia, Chrisina Jayne, Eyad Elyan

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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.
Original languageEnglish
Publication statusAccepted/In press - 10 Apr 2021
EventInternational Joint Conference on Neural Networks 2021 - Virtual
Duration: 18 Jul 202122 Jul 2021


ConferenceInternational Joint Conference on Neural Networks 2021
Abbreviated titleIJCNN 2021
Internet address


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