Towards Arabic Multimodal Dataset for Sentiment Analysis

Abdelhamid Haouhat, Slimane Bellaouar, Attia Nehar, Hadda Cherroun
6/10/2023
cs.CLcs.LG

No official code found. Best implementation: belgats/Arabic-Multimodal-Dataset (7 stars, last commit 2024-10-13).

Abstract

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

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Code Implementations(1)

belgats/Arabic-Multimodal-DatasetCommunity100%

Arabic Multimodal Dataset for Sentiment Analysis

70Shell, PythonMar 1, 20232 years ago

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Cite this paper

@article{haouhat2023towards,
  title  = {Towards Arabic Multimodal Dataset for Sentiment Analysis},
  author = {Abdelhamid Haouhat and Slimane Bellaouar and Attia Nehar and Hadda Cherroun},
  year   = {2023},
  eprint = {2306.06322},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.06322v1}
}

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