Empirical Evaluation of Leveraging Named Entities for Arabic Sentiment Analysis

TL;DR

This paper investigates how sentiment-annotated Named Entities can enhance Arabic sentiment analysis, finding that they improve lexicon-based models but have limited effect on supervised models.

Contribution

It introduces a novel algorithm for detecting sentiment in Named Entities and demonstrates their impact on lexicon-based Arabic sentiment analysis models.

Findings

Named Entities improve lexicon-based model performance

Limited impact of Named Entities on supervised models

Lexicon-based approach outperforms baseline systems

Abstract

Social media reflects the public attitudes towards specific events. Events are often related to persons, locations or organizations, the so-called Named Entities. This can define Named Entities as sentiment-bearing components. In this paper, we dive beyond Named Entities recognition to the exploitation of sentiment-annotated Named Entities in Arabic sentiment analysis. Therefore, we develop an algorithm to detect the sentiment of Named Entities based on the majority of attitudes towards them. This enabled tagging Named Entities 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 Named Entities have no considerable impact on the supervised model, while employing them in the lexicon-based model improved the classification performance and outperformed most of the baseline systems.