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Multimodal Sentiment Analysis for Arabic

#NLP #Multimodal #Sentiment Analysis #Arabic #Deep Learning

Project Overview

Extended sentiment analysis beyond text alone by incorporating additional modalities (e.g. audio tone, visual cues) for Arabic content, addressing cases where sentiment expressed in speech or video is not fully captured by transcribed text, such as sarcasm or dialectal expressiveness carried in tone.

Technical Details

  • Modalities: Text combined with [audio features / visual features, specify which you actually used].
  • Fusion approach: [Early fusion / late fusion / attention-based fusion, specify].
  • Data: [Dataset used, e.g. Arabic video/speech dataset with sentiment labels].

Challenges

Arabic multimodal datasets are scarce compared to English (e.g. CMU-MOSEI), and dialectal variation in speech compounds the difficulty already present in text-only Arabic sentiment work. Aligning modalities meaningfully (rather than just concatenating features) was a core part of the work.

Outcome

[Status: in progress / completed — update this line.] [Add your actual result, e.g. “Multimodal fusion improved F1 by X points over the text-only baseline from the earlier transformer sentiment project.”]

My Role

[Describe your specific contribution, e.g. designed the fusion architecture, built the audio/visual feature extraction pipeline.]

Builds directly on Arabic Sentiment Analysis with Transformers, extending the text-only approach to additional modalities.