Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/1012
Title: | Empirical Evaluation of Leveraging Named Entities for Arabic Sentiment Analysis | Authors: | Mulki, Hala Haddad, Hatem Gridach, Mourad Babaoglu, İsmail |
Keywords: | Named entity recognition Arabic sentiment analysis supervised learning method lexicon-based method |
Publisher: | ZARKA PRIVATE UNIV | Abstract: | Social media reflects the attitudes of the public towards specific events. Events are often related to persons, locations or organizations, the so-called Named Entities (NEs). This can define NEs as sentiment-bearing components. In this paper, we dive beyond NEs recognition to the exploitation of sentiment-annotated NEs in Arabic sentiment analysis. Therefore, we develop an algorithm to detect the sentiment of NEs based on the majority of attitudes towards them. This enabled tagging NEs with proper tags and, thus, including them in a sentiment analysis framework of two models: supervised and lexicon-based. Both models were applied on datasets of multi-dialectal content. The results revealed that NEs have no considerable impact on the supervised model, while employing NEs in the lexicon-based model improved the classification performance and outperformed most of the baseline systems. | URI: | https://doi.org/10.34028/iajit/17/2/11 https://hdl.handle.net/20.500.13091/1012 |
ISSN: | 1683-3198 |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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