United Arab Emirates

Emoji (digital pictograms) are crucial features for textual sentiment analysis. However, analysing the sentiment roles of emoji is very complex. This is due to its dependency on different factors, such as textual context, cultural perspective, interlocutor&#39;s personal traits,  interlocutors&#39; relationships or a platforms&#39; functional features. This work introduces an approach to analysing the sentiment effects of emoji as textual features. Using an Arabic dataset as a benchmark, our results confirm the borrowed argument that each emoji has three different norms of sentiment role (negative, neutral or positive). Therefore, an emoji can play different sentiment roles depending upon the context. It can behave as an emphasizer, an indicator, a mitigator, a reverser or a trigger of either negative or positive sentiment within a text. In addition, an emoji may have a neutral effect (i.e., no effect) on the sentiment of the text.

EMNLP 2022

Emoji Sentiment Roles for Sentiment Analysis: A Case Study in Arabic Texts

emoji

arabic

sentiment analysis

Emoji (digital pictograms) are crucial features for textual sentiment analysis. However, analysing the sentiment roles of emoji is very complex. This is due to its dependency on different factors, such as textual context, cultural perspective, interlocutor's personal traits,  interlocutors' relationships or a platforms' functional features. This work introduces an approach to analysing the sentiment effects of emoji as textual features. Using an Arabic dataset as a benchmark, our results confirm the borrowed argument that each emoji has three different norms of sentiment role (negative, neutral or positive). Therefore, an emoji can play different sentiment roles depending upon the context. It can behave as an emphasizer, an indicator, a mitigator, a reverser or a trigger of either negative or positive sentiment within a text. In addition, an emoji may have a neutral effect (i.e., no effect) on the sentiment of the text.

workshop paper

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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

Arabic diacritic recovery is important for a variety of downstream tasks such as text-to-speech. In this paper, we introduce a new Gulf Arabic diacritization dataset composed of 19,850 words based on a subset of the Gumar corpus.  We provide comprehensive set of guidelines for diacritization to enable the diacritization of more data.  We also report on diacritization results based on the new corpus using a Hidden Markov Model and character-based sequence to sequence models.

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Emoji Sentiment Roles for Sentiment Analysis: A Case Study in Arabic Texts

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Gulf Arabic Diacritization: Guidelines, Initial Dataset, and Results

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Emoji Sentiment Roles for Sentiment Analysis: A Case Study in Arabic Texts

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Next from EMNLP 2022

Gulf Arabic Diacritization: Guidelines, Initial Dataset, and Results

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads