Multi-Modal Sentiment Classification With Independent and Interactive Knowledge via Semi-Supervised Learning

Multi-modal sentiment analysis extends conventional text-based definition of sentiment analysis to a multi-modal setup where multiple relevant modalities are leveraged to perform sentiment analysis. In real applications, however, acquiring annotated multi-modal data is normally labor expensive and t...

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Veröffentlicht in:IEEE access Jg. 8; S. 22945 - 22954
Hauptverfasser: Zhang, Dong, Li, Shoushan, Zhu, Qiaoming, Zhou, Guodong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Annotations
Classification
Data acquisition
Data mining
Decoding
Empirical analysis
Feature extraction
Modal data
multimedia computing
Natural language processing
Semi-supervised learning
Semisupervised learning
Sentiment analysis
Supervised learning
Videos
ISSN:2169-3536, 2169-3536
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Zusammenfassung:Multi-modal sentiment analysis extends conventional text-based definition of sentiment analysis to a multi-modal setup where multiple relevant modalities are leveraged to perform sentiment analysis. In real applications, however, acquiring annotated multi-modal data is normally labor expensive and time-consuming. In this paper, we aim to reduce the annotation effort for multi-modal sentiment classification via semi-supervised learning. The key idea is to leverage the semi-supervised variational autoencoders to mine more information from unlabeled data for multi-modal sentiment analysis. Specifically, the mined information includes both the independent knowledge within single modality and the interactive knowledge among different modalities. Empirical evaluation demonstrates the great effectiveness of the proposed semi-supervised approach to multi-modal sentiment classification.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2969205