Empirical Evaluation of Leveraging Named Entities for Arabic Sentiment Analysis

Empirical Evaluation of Leveraging Named Entities

for Arabic Sentiment Analysis

Hala Mulki1, Hatem Haddad2, Mourad Gridach3, and Ismail Babaoğlu1

1Computer Engineering Department, Konya Technical University, Turkey

2Computer Science Department, University of Manouba, Tunisia

3Computational Bioscience Departments, University of Colorado Boulder, USA

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.

Keywords: Named entity recognition, Arabic sentiment analysis, supervised learning method, lexicon-based method.

Received August 2, 2018; accepted May 21, 2019
https://doi.org/10.34028/iajit/17/2/11

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