  Sentiment analysis serves as a pivotal component in Natural Language
Processing (NLP). Advancements in multilingual pre-trained models such as XLM-R
and mT5 have contributed to the increasing interest in cross-lingual sentiment
analysis. The recent emergence in Large Language Models (LLM) has significantly
advanced general NLP tasks, however, the capability of such LLMs in
cross-lingual sentiment analysis has not been fully studied. This work
undertakes an empirical analysis to compare the cross-lingual transfer
capability of public Small Multilingual Language Models (SMLM) like XLM-R,
against English-centric LLMs such as Llama-3, in the context of sentiment
analysis across English, Spanish, French and Chinese. Our findings reveal that
among public models, SMLMs exhibit superior zero-shot cross-lingual performance
relative to LLMs. However, in few-shot cross-lingual settings, public LLMs
demonstrate an enhanced adaptive potential. In addition, we observe that
proprietary GPT-3.5 and GPT-4 lead in zero-shot cross-lingual capability, but
are outpaced by public models in few-shot scenarios.
