  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 feature extraction techniques, the effect of ensembling,
task-specific deep learning modeling, and domain-independent large language
models (LLMs). We find that while a fine-tuned LLM achieves the best accuracy,
some alternate configurations provide huge (up to 24, 283 *) resource savings
for a marginal (<1%) loss in accuracy. Furthermore, we find that for smaller
datasets, the differences in accuracy shrink while the difference in resource
consumption grows further.
