Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Contrastive Learning-Based Sentiment Analysis

Version 1 : Received: 7 April 2024 / Approved: 8 April 2024 / Online: 8 April 2024 (08:55:21 CEST)

How to cite: Johnson, E.; Nasir, W.; Smith, C. Contrastive Learning-Based Sentiment Analysis. Preprints 2024, 2024040515. https://doi.org/10.20944/preprints202404.0515.v1 Johnson, E.; Nasir, W.; Smith, C. Contrastive Learning-Based Sentiment Analysis. Preprints 2024, 2024040515. https://doi.org/10.20944/preprints202404.0515.v1

Abstract

Recent advancements in machine learning have ushered in innovative techniques for augmenting datasets, particularly through contrastive learning in the computer vision domain. This study pioneers the application of contrastive learning for sentiment analysis, introducing a novel approach termed EmoConLearn. By fine-tuning contrastive learning embeddings, EmoConLearn significantly surpasses BERT-based embeddings in sentiment analysis accuracy, as evidenced by our evaluations on the DynaSent dataset. This research further delves into the efficacy of EmoConLearn across various domain-specific datasets, highlighting its versatility. Additionally, we investigate upsampling strategies to mitigate class imbalance, further enhancing EmoConLearn's performance in sentiment analysis benchmarks.

Keywords

sentiment analysis, contrastive learning, cross-domain adaptation

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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