Johnson, E., Nasir, W., & Smith, C. (2024). Contrastive Learning-Based Sentiment Analysis. Preprints. https://doi.org/10.20944/preprints202404.0515.v1
Chicago/Turabian Style
Johnson, E., Wyne Nasir and Christopher Smith. 2024 "Contrastive Learning-Based Sentiment Analysis" Preprints. 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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
