A.S. Alqarafi
Toward's Arabic multi-modal sentiment analysis
Alqarafi, A.S.; Adeel, A.; Gogate, M.; Dashitpour, K.; Hussain, A.; Durrani, T.
Authors
A. Adeel
Dr. Mandar Gogate M.Gogate@napier.ac.uk
Senior Research Fellow
K. Dashitpour
Prof Amir Hussain A.Hussain@napier.ac.uk
Professor
T. Durrani
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.
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 |
Files
Towards Arabic multi-modal sentiment analysis
(438 Kb)
PDF
Licence
http://creativecommons.org/licenses/by-nc-sa/4.0/
Copyright Statement
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike Licence.
You might also like
A novel brain-inspired compression-based optimised multimodal fusion for emotion recognition
(2018)
Presentation / Conference Contribution
DNN driven speaker independent audio-visual mask estimation for speech separation
(2018)
Presentation / Conference Contribution
Deep learning driven multimodal fusion for automated deception detection
(2018)
Presentation / Conference Contribution
Exploiting Deep Learning for Persian Sentiment Analysis
(2018)
Presentation / Conference Contribution
Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection
(2018)
Presentation / Conference Contribution
Downloadable Citations
About Edinburgh Napier Research Repository
Administrator e-mail: repository@napier.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search