Empirical Evaluation of Leveraging Named Entities for Arabic Sentiment Analysis
This work addresses sentiment analysis for Arabic social media, but it is incremental as it builds on existing Named Entity recognition methods.
The paper tackled the problem of improving Arabic sentiment analysis by incorporating sentiment-annotated Named Entities, finding that this approach had no significant impact on a supervised model but improved performance in a lexicon-based model, outperforming most baselines.
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.