Efficient Sentiment Analysis: A Resource-Aware Evaluation of Feature Extraction Techniques, Ensembling, and Deep Learning Models

arXiv:2308.02022v2 Announce Type: replace
Abstract: While reaching for NLP systems that maximize accuracy, other important metrics of system performance are often overlooked. Prior models are easily forgotten despite their possible suitability in settings where large computing resources are unavailable or relatively more costly. In this paper, we perform a broad comparative evaluation of document-level sentiment analysis models with a focus on resource costs that are important for the feasibility of model deployment and general climate consciousness. Our experiments consider different …

abstract accuracy analysis arxiv computing computing resources cs.cl deep learning evaluation extraction feature feature extraction metrics nlp nlp systems performance prior resources sentiment sentiment analysis systems type