Toward's Arabic multi-modal sentiment analysis

Alqarafi, A.S.; Adeel, A.; Gogate, M.; Dashtipour, K.; Hussain, A.; Durrani, T.

Abstract

In everyday life, people use internet to express and share opinions, facts, and sentiments about products and services. In addition, social media applications such as Facebook, Twitter, WhatsApp, Snapchat etc., have become important information sharing platforms. Apart from these, a collection of product reviews, facts, poll information, etc., is a need for every company or organization ranging from start-ups to big firms and governments. Clearly, it is very challenging to analyse such big data to improve products, services, and satisfy customer requirements. Therefore, it is necessary to automate the evaluation process using advanced sentiment analysis techniques. Most of previous works focused on uni-modal sentiment analysis mainly textual model. In this paper, a novel Arabic multimodal dataset is presented and validated using state-of-the-art support vector machine (SVM) based classification method.

Citation

Alqarafi, A., Adeel, A., Gogate, M., Dashtipour, K., Hussain, A., & Durrani, T. (2017, July). Toward's Arabic multi-modal sentiment analysis. Presented at International Conference in Communications, Signal Processing, and Systems, Harbin, China

• Presentation Conference Type: Conference Paper (published)
• Conference Name: International Conference in Communications, Signal Processing, and Systems
• Start Date: Jul 14, 2017
• End Date: Jul 16, 2017
• Online Publication Date: Jun 7, 2018
• Publication Date: 2019
• Deposit Date: Jul 19, 2019
• Publicly Available Date: Jul 19, 2019
• Journal: Communications, Signal Processing, and Systems
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 2378-2386
• Series Title: Lecture Notes in Electrical Engineering
• Series Number: 463
• DOI: https://doi.org/10.1007/978-981-10-6571-2_290
• Public URL: http://researchrepository.napier.ac.uk/Output/1792086
• Related Public URLs: https://www.storre.stir.ac.uk/handle/1893/27750
• Contract Date: Jul 19, 2019