Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3582
Title: Dealing with Class Imbalance in Sentiment Analysis using Deep Learning and SMOTE
Authors: Kedas, Shweta
Kumar, Arun
Jain, Puneet Kumar
Keywords: Deep Learning
SMOTE
Issue Date: Sep-2021
Citation: International Conference on Advances in Data Computing, Communication and Security, (I3CS2021) , Sep 08-10, 2021
Abstract: In textual data, sentiments or opinions expressing polarities (positive or negative) often form the basis for human decision-making. Therefore, sentiment analysis has always been an important area of research in the field of artificial in- telligence. Recently, deep learning models have been used for the sentiment analy- sis. However, the class imbalance of the dataset adversely affects the performance of these models. To address this issue, the paper presents a method to resample the dataset using Synthetic Minority Over-sampling Technique (SMOTE). The pro- posed method is applied to three different datasets of customer reviews, each of which exhibits different class imbalance ratios. To show the impact of the method, the modern Recurrent Neural Network (RNN) based architectures (LSTM, GRU, and Bi-directional) are trained with both the originally imbalanced datasets and the SMOTE balanced datasets, and comprehensively analysed the performance of these approaches. The obtained results show that the models trained with balanced datasets outperform other methods across most models with significant improve- ments over the original dataset.
Description: Copyright of this paper belongs to the conference publisher.
URI: http://hdl.handle.net/2080/3582
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
Arun,k_I3CS2021.pdf814.93 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.