Journal article
Multimodal Emotion and Sentiment Modeling from Unstructured Big Data: Challenges, Architecture, Techniques
IEEE Access, Vol.7, pp.90982-90998
2019
Abstract
The exponential growth of multimodal content in today's competitive business environment leads to a huge volume of unstructured data. Unstructured big data has no particular format or structure and can be in any form, such as text, audio, images, and video. In this paper, we address the challenges of emotion and sentiment modeling due to unstructured big data with different modalities. We first include an up-to-date review on emotion and sentiment modeling including the state-of-the-art techniques.We then propose a new architecture of multimodal emotion and sentiment modeling for big data. The proposed architecture consists of five essential modules: data collection module, multimodal data aggregation module, multimodal data feature extraction module, fusion and decision module, and application module. Novel feature extraction techniques called the divide-and-conquer principal component analysis (Div-ConPCA) and the divide-andconquer linear discriminant analysis (Div-ConLDA) are proposed for the multimodal data feature extraction module in the architecture. The experiments on a multicore machine architecture are performed to validate the performance of the proposed techniques.
Details
- Title
- Multimodal Emotion and Sentiment Modeling from Unstructured Big Data: Challenges, Architecture, Techniques
- Authors
- J K P Seng (Author) - University of New South WalesLi-Minn Ang (Author) - Griffith University
- Publication details
- IEEE Access, Vol.7, pp.90982-90998
- Publisher
- Institute of Electrical and Electronics Engineers
- Date published
- 2019
- DOI
- 10.1109/ACCESS.2019.2926751
- ISSN
- 2169-3536
- Copyright note
- Copyright © The Author. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
- Organisation Unit
- School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
- Language
- English
- Record Identifier
- 99450809602621
- Output Type
- Journal article
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- Collaboration types
- Domestic collaboration
- Web Of Science research areas
- Computer Science, Information Systems
- Engineering, Electrical & Electronic
- Telecommunications