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Toward's Arabic multi-modal sentiment analysis

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


A.S. Alqarafi

A. Adeel

K. Dashitpour

T. Durrani


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.

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
Public URL
Related Public URLs
Contract Date Jul 19, 2019


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