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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Fusing audio, visual and textual clues for sentiment analysis from multimodal content
Author(s): Poria, Soujanya
Cambria, Erik
Howard, Newton
Huang, Guang-Bin
Hussain, Amir
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Keywords: Multimodal fusion
Big social data analysis
Opinion mining
Multimodal sentiment analysis
Sentic computing
Issue Date: 22-Jan-2016
Date Deposited: 12-Jul-2016
Citation: Poria S, Cambria E, Howard N, Huang G & Hussain A (2016) Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing, 174 (A), pp. 50-59.
Abstract: A huge number of videos are posted every day on social media platforms such as Facebook and YouTube. This makes the Internet an unlimited source of information. In the coming decades, coping with such information and mining useful knowledge from it will be an increasingly difficult task. In this paper, we propose a novel methodology for multimodal sentiment analysis, which consists in harvesting sentiments from Web videos by demonstrating a model that uses audio, visual and textual modalities as sources of information. We used both feature- and decision-level fusion methods to merge affective information extracted from multiple modalities. A thorough comparison with existing works in this area is carried out throughout the paper, which demonstrates the novelty of our approach. Preliminary comparative experiments with the YouTube dataset show that the proposed multimodal system achieves an accuracy of nearly 80%, outperforming all state-of-the-art systems by more than 20%.
DOI Link: 10.1016/j.neucom.2015.01.095
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