Source of Publication
Procedia Computer Science
© 2017 The Authors. Published by Elsevier B.V. There is an increased interest in social media monitoring to analyse massive, free form, short user-generated text from multiple social media sites such as Facebook, WhatsApp and Twitter. Companies are interested in sentiment analysis to understand customers' opinions about their products/services. Governments and law enforcement agencies are interested in identifying threats to safeguard their country's national security. They are actively seeking ways to monitor and analyse the public's responses to various services, activities and events, especially since social media has become a valuable real-time resource of information. This study builds on prior work that focused on sentiment classification (i.e., positive, negative). This study primarily aims to design and develop a social sentiment-parsing algorithm for capturing and monitoring an extensive and comprehensive range of emotions from Arabic social media text. The study contributes to the field of sentiment analysis (opinion mining) and can subsequently be used for web mining, cleansing and analytics.
Arabic, emotions, ontology, sentiment, sentiment parsing technique, social media
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Bani-Hani, Anoud; Majdalweieh, Munir; and Obeidat, Feras, "The Creation of an Arabic Emotion Ontology Based on E-Motive" (2017). All Works. 3371.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series