Ontology-based Emotion Detection in Arabic Social Media
Sawsan N. Cassab, Mohamad-Bassam Kurdy · Zenodo (CERN European Organization for Nuclear Research) · 2020
With the spread of social media and its content that is often driven by user's emotions or their opinions on some topic, efforts have been made to provide effectiveness mechanisms for automatic emotion detection, to employ it in various fields. With the spread of Arabic language on those sites, however Arabic studies in emotion detection are still shy somewhat, perhaps, due to the difficult dealing with Arabic language especially with slang that is used mainly in social media, in the absence of unified representation of emotion in Arabic, this was motivation to build an ArEmontology ontology, for conceptual representation of emotions in Arabic with standard language based on emotional theories, in addition to propose an effective mechanism to detect emotion from Arabic text by using the classification and semantic relations in ArEmontology, this mechanism was applied on Facebook 's posts, as it is the most site used by Syrian, and got reasonable accuracy of 65% in detecting one of the emotional categories: joy, sadness, fear, anger, surprised and disgust.