Modelling Sentiment Analysis: LLMs and data augmentation techniques
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Abstract (arXiv)
This paper provides different approaches for a binary sentiment classification on a small training dataset. LLMs that provided state-of-the-art results in sentiment analysis and similar domains are being used, such as BERT, RoBERTa and XLNet.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 9 references that are in the map. First posted on arXiv 7 Nov 2023.
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References · 5
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 377
- 2020Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Taskscited by 138
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