In recent years, multimodal natural language processing, aimed at learning
from diverse data types, has garnered significant attention. However, there
needs to be more clarity when it comes to analysing multimodal tasks in
multi-lingual contexts. While prior studies on sentiment analysis of tweets
have predominantly focused on the English language, this paper addresses this
gap by transforming an existing textual Twitter sentiment dataset into a
multimodal format through a straightforward curation process. Our work opens up
new avenues for sentiment-related research within the research community.
Additionally, we conduct baseline experiments utilising this augmented dataset
and report the findings. Notably, our evaluations reveal that when comparing
unimodal and multimodal configurations, using a sentiment-tuned large language
model as a text encoder performs exceptionally well.

近年来，多模态自然语言处理引起了广泛关注，但我们需要更清晰地分析多语言环境下的多模态任务。本文通过一个简单的策划过程，将一份现有的文本 Twitter 情感数据集转化为多模态格式，从而填补了先前主要关注英语的情感分析研究的空白，并为研究界开辟了情感相关研究的新领域。此外，我们利用这个增强的数据集进行了基准实验，并报告了结果。值得注意的是，我们的评估结果显示，在单模态和多模态配置相比较时，使用一个经过情感调整的大型语言模型作为文本编码器表现出色。