  The aspect-based sentiment analysis (ABSA) is a standard NLP task with
numerous approaches and benchmarks, where large language models (LLM) represent
the current state-of-the-art. We focus on ABSA subtasks based on Twitter/X data
in underrepresented languages. On such narrow tasks, small tuned language
models can often outperform universal large ones, providing available and cheap
solutions.
  We fine-tune several LLMs (BERT, BERTweet, Llama2, Llama3, Mistral) for
classification of sentiment towards Russia and Ukraine in the context of the
ongoing military conflict. The training/testing dataset was obtained from the
academic API from Twitter/X during 2023, narrowed to the languages of the V4
countries (Czech Republic, Slovakia, Poland, Hungary). Then we measure their
performance under a variety of settings including translations, sentiment
targets, in-context learning and more, using GPT4 as a reference model. We
document several interesting phenomena demonstrating, among others, that some
models are much better fine-tunable on multilingual Twitter tasks than others,
and that they can reach the SOTA level with a very small training set. Finally
we identify combinations of settings providing the best results.
