Improving Arabic Sarcasm Detection by Excluding Emojis from Social Media Content

Kernekoncepter

Excluding emojis from Arabic social media datasets can significantly improve the performance of sarcasm detection models, as emojis may introduce ambiguity and noise that degrade the models' ability to accurately classify text.

Resumé

This study investigates the impact of excluding emojis from Arabic social media datasets on the performance of sarcasm detection models. The researchers used three pre-trained models - AraBERT-v2, AraBERTv02-twitter, and Multi-dialect-BERT-base-Arabic - and evaluated their performance on three datasets: SemEval 2020, YouTube, and L-HSAB.

The results show that excluding emojis from the datasets consistently improves the accuracy, recall, precision, and F1-score of the models across the different datasets and models. This suggests that emojis can introduce ambiguity or noise that degrades the models' ability to accurately classify sarcastic content in Arabic social media posts.

The researchers found that the exclusion of emojis led to increased recall, particularly for the TW and MD models, indicating that emojis may have a negative impact on the models' ability to detect sarcastic instances. Precision also notably increased, especially for the V2 and MD models, when emojis were removed, reducing the number of false positives.

Similarly, the F1-score improved across all models when emojis were excluded, except for a slight drop in the V2 model, suggesting a better balance between precision and recall. These findings support the hypothesis that removing emojis from the preprocessing stage can enhance the performance of sarcasm detection models in Arabic social media content.

The study establishes new benchmarks in Arabic natural language processing and provides valuable insights for social media platforms seeking to improve their content moderation capabilities, particularly in detecting and addressing sarcastic speech.

Statistik

The number of roots in Arabic is approximately 23,090, which is significantly higher than other languages like English (8,400) and Russian (450).

Citater

"Emojis have demonstrated their effectiveness in partially bridging the gap between textual and vocal/visual communication."
"Emojis introduce ambiguity or noise that degrades classification accuracy, while their exclusion leads to increased recall, particularly for TW and MD models."
"Precision notably increases, especially for V2 and MD models, when emojis are removed, reducing false positives."

Vigtigste indsigter udtrukket fra

Impact of emoji exclusion on the performance of Arabic sarcasm detection models

by Ghalyah H. A... kl. arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.02195.pdf

Dybere Forespørgsler

How can the insights from this study be applied to improve sarcasm detection in other languages with rich vocabularies and diverse dialects?

The insights from this study can be applied to improve sarcasm detection in other languages with rich vocabularies and diverse dialects by focusing on the impact of non-textual elements, such as emojis, on the performance of sarcasm detection models. Understanding the nuances of sarcasm in different languages and dialects is crucial for developing accurate detection models. By excluding emojis or other non-textual features that may introduce noise or ambiguity, the models can focus more on the linguistic and contextual aspects of the text, leading to improved accuracy in detecting sarcasm. Additionally, the study highlights the importance of data preprocessing and the selection of appropriate machine learning models tailored for specific languages, which can be applied to other languages with similar characteristics.

What other non-textual features, besides emojis, might impact the performance of sarcasm detection models, and how can they be effectively incorporated or excluded?

In addition to emojis, other non-textual features that might impact the performance of sarcasm detection models include tone of voice, intonation, punctuation, and context. Tone of voice and intonation play a significant role in conveying sarcasm in spoken language, which can be challenging to capture in written text. Punctuation, such as exclamation marks or question marks, can also influence the interpretation of sarcasm. Contextual cues, cultural references, and sarcasm markers specific to certain languages or dialects are essential for accurate detection.
To effectively incorporate or exclude these non-textual features, researchers can explore multimodal approaches that combine text with audio or visual cues. For example, incorporating speech recognition technology to analyze tone of voice or intonation in spoken language can enhance sarcasm detection. Additionally, developing models that can analyze punctuation patterns and contextual information in conjunction with text data can improve the overall performance of sarcasm detection systems. By carefully considering these non-textual features and their impact on sarcasm detection, researchers can develop more robust and accurate models.

How can the findings from this research be leveraged to develop more comprehensive and culturally-aware content moderation systems for social media platforms in the Arab world?

The findings from this research can be leveraged to develop more comprehensive and culturally-aware content moderation systems for social media platforms in the Arab world by enhancing the detection of offensive language, hate speech, and sarcasm. By understanding the impact of emojis and other non-textual features on sarcasm detection, researchers can refine existing models and algorithms to better analyze and interpret Arabic text in social media content. This can lead to more accurate identification of offensive or harmful language, enabling platforms to effectively moderate and filter out inappropriate content.
Furthermore, the study's emphasis on data preprocessing, model selection, and performance evaluation can guide the development of tailored solutions for Arabic language processing and content moderation. By incorporating insights from this research, social media platforms can implement advanced machine learning techniques, such as transfer learning and fine-tuning pre-trained models, to improve the detection of sarcasm and other linguistic nuances specific to the Arabic language. This can contribute to creating a safer and more inclusive online environment for users in the Arab world.