Machine learning-based model for sentiment and sarcasm detection

Aya H. Allam, Hanya M. Abdallah, Eslam Amer, Hamada A. Nayel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Within the last few years, the number of Arabic internet users and Arabic online content is in exponential growth. Dealing with Arabic datasets and the usage of non-explicit sentences to express an opinion are considered to be the major challenges in the field of natural language processing. Hence, sarcasm and sentiment analysis has gained a major interest from the research community, especially in this language. Automatic sarcasm detection and sentiment analysis can be applied using three approaches, namely supervised, unsupervised and hybrid approach. In this paper, a model based on a supervised machine learning algorithm called Support Vector Machine (SVM) has been used for this process. The proposed model has been evaluated using ArSarcasm-v2 dataset. The performance of the proposed model has been compared with other models submitted to sentiment analysis and sarcasm detection shared task.

Original languageEnglish
Title of host publicationWANLP 2021 - 6th Arabic Natural Language Processing Workshop, Proceedings of the Workshop
EditorsNizar Habash, Houda Bouamor, Hazem Hajj, Walid Magdy, Wajdi Zaghouani, Fethi Bougares, Nadi Tomeh, Ibrahim Abu Farha, Samia Touileb
PublisherAssociation for Computational Linguistics
Pages386-389
Number of pages4
ISBN (Electronic)9781954085091
Publication statusPublished - 19 Apr 2021
Event6th Arabic Natural Language Processing Workshop, WANLP 2021 - Virtual, Kyiv, Ukraine
Duration: 19 Apr 2021 → …

Conference

Conference6th Arabic Natural Language Processing Workshop, WANLP 2021
Country/TerritoryUkraine
CityVirtual, Kyiv
Period19/04/21 → …

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