Conference Proceeding

Towards Arabic multi-modal sentiment analysis


Alqarafi AS, Adeel A, Gogate M, Dashtipour K, Hussain A & Durrani T (2019) Towards Arabic multi-modal sentiment analysis. In: Liang Q, Mu J, Jia M, Wang W, Feng X & Zhang B (eds.) Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, 463. CSPS 2017: Communications, Signal Processing, and Systems, 14.07.2017-16.07.2017. Harbin, China: Springer, pp. 2378-2386.

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.

Arabic; Sentiment analysis; Multi-modal

Lecture Notes in Electrical Engineering

FundersEngineering and Physical Sciences Research Council
Title of seriesLecture Notes in Electrical Engineering
Number in series463
Publication date31/12/2019
Publication date online07/06/2018
Place of publicationHarbin, China
ISSN of series1876-1100
ISBN978-981-10-6570-5; 978-981-10-6571-2
ConferenceCSPS 2017: Communications, Signal Processing, and Systems