Arabic Metaphor Sentiment Classification Using Semantic Information

Israa Alsiyat
4/28/2025
cs.CLcs.AI

No confident code implementation found for this paper.

Abstract

In this paper, I discuss the testing of the Arabic Metaphor Corpus (AMC) [1] using newly designed automatic tools for sentiment classification for AMC based on semantic tags. The tool incorporates semantic emotional tags for sentiment classification. I evaluate the tool using standard methods, which are F-score, recall, and precision. The method is to show the impact of Arabic online metaphors on sentiment through the newly designed tools. To the best of our knowledge, this is the first approach to conduct sentiment classification for Arabic metaphors using semantic tags to find the impact of the metaphor.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

A comprehensive survey of contemporary Arabic sentiment analysis: Methods, Challenges, and Future Directions

82% match2025

Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysis

80% match2024

Towards Arabic Multimodal Dataset for Sentiment Analysis

79% match1 repo72023

Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss Function

78% match1 repo2024

AHaSIS: Shared Task on Sentiment Analysis for Arabic Dialects

78% match2025

A Novel Dialect-Aware Framework for the Classification of Arabic Dialects and Emotions

78% match2025

Cite this paper

@article{alsiyat2025arabic,
  title  = {Arabic Metaphor Sentiment Classification Using Semantic Information},
  author = {Israa Alsiyat},
  year   = {2025},
  eprint = {2504.19590},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.19590v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2504.19590.svg)](https://codeofpaper.com/paper/2504.19590)

Discussion