Papers › Modelling Sentiment Analysis: LLMs and data augmentation techniques

Modelling Sentiment Analysis: LLMs and data augmentation techniques

7 Nov 2023arXiv:2311.04139archive 2025-07-28

Guillem Senabre Prades

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.

Paper PDF Code

Code

guillemsenabre/Enhancing-Sentiment-analysis-with-LLM-and-data-augmentation-techniques report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Data Augmentation Sentiment Analysis Sentiment Classification

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

Adam Attention Attention Dropout BERT BPE Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection RoBERTa SentencePiece Softmax Weight Decay WordPiece XLNet

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections