|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Author(s):||Alqarafi, Abdulrahman S|
|Title:||Towards Arabic multi-modal sentiment analysis|
|Citation:||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. https://doi.org/10.1007/978-981-10-6571-2_290|
|Series/Report no.:||Lecture Notes in Electrical Engineering, 463|
|Conference Name:||CSPS 2017: Communications, Signal Processing, and Systems|
|Conference Dates:||2017-07-14 - 2017-07-16|
|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.|
|Status:||AM - Accepted Manuscript|
|Rights:||Accepted for publication in Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, 463. CSPS 2017: Communications, Signal Processing, and Systems, Harbin, China, 14.07.2017-16.07.2017. Harbin, China: Springer Verlag, pp. 2378-2386. The final publication is available at Springer via https://doi.org/10.1007/978-981-10-6571-2_290.|
|Abdulrahman Alqarafi CSPS Paper.pdf||Fulltext - Accepted Version||427.88 kB||Adobe PDF||View/Open|
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