Fine-tuning multilingual language models in Twitter/X sentiment analysis: a study on Eastern-European V4 languages
<|reference_start|>Fine-tuning multilingual language models in Twitter/X sentiment analysis: a study on Eastern-European V4 languages: 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.<|reference_end|>
@article{filip2024fine-tuning,
    title={Fine-tuning multilingual language models in Twitter/X sentiment
  analysis: a study on Eastern-European V4 languages},
    author={Tom'av{s} Filip, Martin Pavl'iv{c}ek, Petr Sos'ik},
    journal={arXiv preprint arXiv:2408.02044},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02044},
    primaryClass={cs.CL cs.AI}
}