Efficient Sentiment Analysis: Resource-Aware Evaluation Of Models
Researchers evaluated document-level sentiment analysis models, finding fine-tuned LLM achieves best accuracy but alternate configs offer massive resource savings (up to 24,283x) with minimal loss in accuracy.
This is a Plain English Papers summary of a research paper called Efficient Sentiment Analysis: A Resource-Aware Evaluation of Feature Extraction Techniques, Ensembling, and Deep Learning Models. If you like these kinds of analysis, you should subscribe to the AImodels.fyi newsletter or follow me on Twitter. Overview This paper focuses on evaluating document-level sentiment analysis models, with a particular emphasis on resource costs and the feasibility of model deployment. The researchers consider different feature extraction techniques, the impact of ensembling, task-specific deep...