  Multimodal Sentiment Analysis (MSA) has recently become a centric research
direction for many real-world applications. This proliferation is due to the
fact that opinions are central to almost all human activities and are key
influencers of our behaviors. In addition, the recent deployment of Deep
Learning-based (DL) models has proven their high efficiency for a wide range of
Western languages. In contrast, Arabic DL-based multimodal sentiment analysis
(MSA) is still in its infantile stage due, mainly, to the lack of standard
datasets. In this paper, our investigation is twofold. First, we design a
pipeline that helps building our Arabic Multimodal dataset leveraging both
state-of-the-art transformers and feature extraction tools within word
alignment techniques. Thereafter, we validate our dataset using
state-of-the-art transformer-based model dealing with multimodality. Despite
the small size of the outcome dataset, experiments show that Arabic
multimodality is very promising
