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Impact of Balancing Techniques for Imbalanced Class Distribution on Twitter Data for Emotion Analysis: A Case Study
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Author(s): Shivani Vasantbhai Vora (CGPIT, Uka Tarsadia University, Bardoli, India), Rupa G. Mehta (Sardar Vallabhbhai National Institute of Technology, Surat, India)and Shreyas Kishorkumar Patel (Sardar Vallabhbhai National Institute of Technology, Surat, India)
Copyright: 2021
Pages: 21
Source title:
Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance
Source Author(s)/Editor(s): Dipti P. Rana (Sardar Vallabhbhai National Institute of Technology, Surat, India)and Rupa G. Mehta (Sardar Vallabhbhai National Institute of Technology, Surat, India)
DOI: 10.4018/978-1-7998-7371-6.ch012
PurchaseView on the publisher's website for pricing and purchasing information.
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Abstract
Continuously growing technology enhances creativity and simplifies humans' lives and offers the possibility to anticipate and satisfy their unmet needs. Understanding emotions is a crucial part of human behavior. Machines must deeply understand emotions to be able to predict human needs. Most tweets have sentiments of the user. It inherits the imbalanced class distribution. Most machine learning (ML) algorithms are likely to get biased towards the majority classes. The imbalanced distribution of classes gained extensive attention as it has produced many research challenges. It demands efficient approaches to handle the imbalanced data set. Strategies used for balancing the distribution of classes in the case study are handling redundant data, resampling training data, and data augmentation. Six methods related to these techniques have been examined in a case study. Upon conducting experiments on the Twitter dataset, it is seen that merging minority classes and shuffle sentence methods outperform other techniques.
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