More Web Proxy on the site http://driver.im/

Article

Fusion-Extraction Network for Multimodal Sentiment Analysis

Authors:

Yingbiao LingAuthors Info & Claims

Advances in Knowledge Discovery and Data Mining: 24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11–14, 2020, Proceedings, Part II

Pages 785 - 797

https://doi.org/10.1007/978-3-030-47436-2_59

Published: 11 May 2020 Publication History

Abstract

Multiple modality data bring new challenges for sentiment analysis, as combining varieties of information in an effective manner is a rigorous task. Previous works do not effectively utilize the relationship and influence between texts and images. This paper proposes a fusion-extraction network model for multimodal sentiment analysis. First, our model uses an interactive information fusion mechanism to interactively learn the visual-specific textual representations and the textual-specific visual representations. Then, we propose an information extraction mechanism to extract valid information and filter redundant parts for the specific textual and visual representations. The experimental results on two public multimodal sentiment datasets show that our model outperforms existing state-of-the-art methods.

References

[1]

Agarwal B, Poria S, Mittal N, Gelbukh A, and Hussain A Concept-level sentiment analysis with dependency-based semantic parsing: a novel approach Cogn. Comput. 2015 7 4 487-499

[2]

Borth, D., Ji, R., Chen, T., Breuel, T., Chang, S.F.: Large-scale visual sentiment ontology and detectors using adjective noun pairs. In: In ACM MM. Citeseer (2013)

[3]

Cai, G., Xia, B.: Convolutional neural networks for multimedia sentiment analysis. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds.) NLPCC-2015. LNCS (LNAI), vol. 9362, pp. 159–167. Springer, Cham (2015). 10.1007/978-3-319-25207-0_14

[4]

Cambria, E., Poria, S., Bajpai, R., Schuller, B.: SenticNet 4: a semantic resource for sentiment analysis based on conceptual primitives. In: COLING (2016)

[5]

Cambria, E., Poria, S., Hazarika, D., Kwok, K.: SenticNet 5: discovering conceptual primitives for sentiment analysis by means of context embeddings. In: AAAI (2018)

[6]

Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT (2019)

[7]

Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: EMNLP, pp. 3433–3442 (2018)

[8]

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)

[9]

He K, Zhang X, Ren S, and Sun J Leibe B, Matas J, Sebe N, and Welling M Identity mappings in deep residual networks Computer Vision – ECCV 2016 2016 Cham Springer 630-645

[10]

Johnson, R., Zhang, T.: Semi-supervised convolutional neural networks for text categorization via region embedding. In: NIPS, pp. 919–927 (2015)

[11]

Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: ACL, vol. 1, pp. 655–665 (2014)

[12]

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)

[13]

Li, Z., Wei, Y., Zhang, Y., Yang, Q.: Hierarchical attention transfer network for cross-domain sentiment classification. In: AAAI (2018)

[14]

Liu B Sentiment analysis and opinion mining Synth. Lect. Hum. Lang. Technol. 2012 5 1 1-167

[15]

Niu T, Zhu S, Pang L, and El Saddik A Tian Q, Sebe N, Qi G-J, Huet B, Hong R, and Liu X Sentiment analysis on multi-view social data MultiMedia Modeling 2016 Cham Springer 15-27

[16]

Owoputi, O., O’Connor, B., Dyer, C., Gimpel, K., Schneider, N., Smith, N.A.: Improved part-of-speech tagging for online conversational text with word clusters. In: NAACL-HLT, pp. 380–390 (2013)

[17]

Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)

[18]

Russakovsky O et al.ImageNet large scale visual recognition challengeInt. J. Comput. Vis.20151153211-2523422482

[19]

Szegedy, C., et al.: Going deeper with convolutions. In: CVPR (2015)

[20]

Truong, Q.T., Lauw, H.W.: VistaNet: visual aspect attention network for multimodal sentiment analysis. In: AAAI (2019)

[21]

Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: EMNLP (2005)

[22]

Xu, N., Mao, W.: MultiSentiNet: a deep semantic network for multimodal sentiment analysis. In: CIKM, pp. 2399–2402. ACM (2017)

[23]

Xu, N., Mao, W., Chen, G.: A co-memory network for multimodal sentiment analysis. In: SIGIR, pp. 929–932. ACM (2018)

[24]

Xu, N., Mao, W., Chen, G.: Multi-interactive memory network for aspect based multimodal sentiment analysis. In: AAAI (2019)

[25]

Xue, W., Li, T.: Aspect based sentiment analysis with gated convolutional networks. In: ACL, pp. 2514–2523 (2018)

[26]

You, Q., Jin, H., Luo, J.: Visual sentiment analysis by attending on local image regions. In: AAAI (2017)

[27]

Yu Y, Lin H, Meng J, and Zhao ZVisual and textual sentiment analysis of a microblog using deep convolutional neural networksAlgorithms201692413517461

[28]

Zadeh, A., Chen, M., Poria, S., Cambria, E., Morency, L.P.: Tensor fusion network for multimodal sentiment analysis. In: EMNLP, pp. 1103–1114 (2017)

Cited By

Xie SLi J(2024)A Multimodal Sentiment Analysis Method Integrating Multi-Layer Attention Interaction and Multi-Feature EnhancementInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.33594017:1(1-20)Online publication date: 30-Jan-2024
https://dl.acm.org/doi/10.4018/IJITSA.335940
Li GZeng XChen CZhou L(2024)Multi-attention Fusion for Multimodal Sentiment ClassificationProceedings of 2024 ACM ICMR Workshop on Multimodal Video Retrieval10.1145/3664524.3675360(1-7)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3664524.3675360
Zhong TLang JZhang YCheng ZZhang KZhou FHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Predicting Micro-video Popularity via Multi-modal Retrieval AugmentationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657929(2579-2583)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657929
Show More Cited By

Index Terms

Fusion-Extraction Network for Multimodal Sentiment Analysis
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
    2. Natural language processing
  2. Machine learning
2. Information systems

Index terms have been assigned to the content through auto-classification.

Recommendations

Joint sentiment/topic model for sentiment analysis
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet ...
Sentence compression for aspect-based sentiment analysis

Sentiment analysis, which addresses the computational treatment of opinion, sentiment, and subjectivity in text, has received considerable attention in recent years. In contrast to the traditional coarse-grained sentiment analysis tasks, such as document-...
Dual-Perspective Fusion Network for Aspect-Based Multimodal Sentiment Analysis
Aspect-based multimodal sentiment analysis (ABMSA) is an important sentiment analysis task that analyses aspect-specific sentiment in data with different modalities (usually multimodal data with text and images). Previous works usually ignore the overall ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings

Advances in Knowledge Discovery and Data Mining: 24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11–14, 2020, Proceedings, Part II

May 2020

935 pages

ISBN:978-3-030-47435-5

DOI:10.1007/978-3-030-47436-2

Editors:
Hady W. Lauw
School of Information Systems, Singapore Management University, Singapore, Singapore
,
Raymond Chi-Wing Wong
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong
,
Alexandros Ntoulas
Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece
,
Ee-Peng Lim
School of Information Systems, Singapore Management University, Singapore, Singapore
,
See-Kiong Ng
Institute of Data Science, National University of Singapore, Singapore, Singapore
,
Sinno Jialin Pan
School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

© Springer Nature Switzerland AG 2020.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 11 May 2020

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 04 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Xie SLi J(2024)A Multimodal Sentiment Analysis Method Integrating Multi-Layer Attention Interaction and Multi-Feature EnhancementInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.33594017:1(1-20)Online publication date: 30-Jan-2024
https://dl.acm.org/doi/10.4018/IJITSA.335940
Li GZeng XChen CZhou L(2024)Multi-attention Fusion for Multimodal Sentiment ClassificationProceedings of 2024 ACM ICMR Workshop on Multimodal Video Retrieval10.1145/3664524.3675360(1-7)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3664524.3675360
Zhong TLang JZhang YCheng ZZhang KZhou FHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Predicting Micro-video Popularity via Multi-modal Retrieval AugmentationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657929(2579-2583)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657929
Hu XYamamura M(2022)Two-stage Attention-based Fusion Neural Network for Image-Text Sentiment ClassificationProceedings of the 2022 4th International Conference on Image, Video and Signal Processing10.1145/3531232.3531233(1-7)Online publication date: 18-Mar-2022
https://dl.acm.org/doi/10.1145/3531232.3531233
Chauhan DEkbal ABhattacharyya PAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)An Efficient Fusion Mechanism for Multimodal Low-resource SettingProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531900(2583-2588)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531900
Salur MAydın İ(2022)A soft voting ensemble learning-based approach for multimodal sentiment analysisNeural Computing and Applications10.1007/s00521-022-07451-734:21(18391-18406)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s00521-022-07451-7
Li SDeng WHu J(2022)Momentum Distillation Improves Multimodal Sentiment AnalysisPattern Recognition and Computer Vision10.1007/978-3-031-18907-4_33(423-435)Online publication date: 14-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-18907-4_33
Ligthart ACatal CTekinerdogan B(2021)Systematic reviews in sentiment analysis: a tertiary studyArtificial Intelligence Review10.1007/s10462-021-09973-354:7(4997-5053)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1007/s10462-021-09973-3

View Options

View options

Media

Figures

Other

Tables

View Table of Contents