CLNov 7, 2023
Modelling Sentiment Analysis: LLMs and data augmentation techniques
arXiv:2311.04139v10.5
Originality Synthesis-oriented
AI Analysis
This work addresses sentiment analysis for applications with limited data, but it is incremental as it applies existing methods to a specific scenario.
The paper tackles binary sentiment classification on a small training dataset by using LLMs like BERT, RoBERTa, and XLNet, achieving state-of-the-art results in sentiment analysis.
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.