Modelling Sentiment Analysis: LLMs and data augmentation techniques

Autor:	Prades, Guillem Senabre
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: 4 pages. For more information check the github link in the conclusion. Enjoy!
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.04139 Zobrazit plný text záznamu View this record from Arxiv