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