Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/27750
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Alqarafi, Abdulrahman S
Adeel, Ahsan
Gogate, Mandar
Dashitpour, Kia
Hussain, Amir
Durrani, Tariq
Title: Towards Arabic multi-modal sentiment analysis
Editor(s): Liang, Q
Mu, J
Jia, M
Wang, W
Feng, X
Zhang, B
Citation: Alqarafi AS, Adeel A, Gogate M, Dashitpour 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
Issue Date: 31-Dec-2019
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
Conference Location: Harbin, China
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.

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