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
Issue Date: 2020
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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