The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis
Open accessAbstract
We present a novel way of extracting features from short texts, based on the activation values of an inner layer of a deep convolutional neural network.We use the extracted features in multimodal sentiment analysis of short video clips representing one sentence each.We use the combined feature vectors of textual, visual, and audio modalities to train a classifier based on multiple kernel learning, which is known to be good at heterogeneous data.We obtain 14% performance improvement over the state of the art and present a parallelizable decision-level data fusion method, which is much faster, though slightly less accurate.
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Cites
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831
Linguistic Regularities in Continuous Space Word Representations
Mikolov, T., Yih, W.-T., & Zweig, G. (2013) · Cited by 2,912
Opensmile: the munich versatile and fast open-source audio feature extractor
Eyben, F., Wöllmer, M., & Schuller, B. (2010) · Proceedings of the 18th ACM international conference on Multimedia · doi:10.1145/1873951.1874246 · Cited by 2,642
Fusing audio, visual and textual clues for sentiment analysis from multimodal content
Poria, S., Cambria, E., Howard, N., Huang, G.-B., & Hussain, A. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.01.095 · Cited by 534 · cited 2×
“While the visual and the audio modalities provide additional evidence that improves classification accuracy, we found the textual modality to have the greater impact on the result (Cambria and Hussain, 2015; Cambria et al., 2013c; Poria et al., 2015a; 2015b).”
BackgroundSelf-citationTowards multimodal sentiment analysis: harvesting opinions from the web
Morency, L.-P., Mihalcea, R., & Doshi, P. (2011) · Proceedings of the 13th international conference on multimodal interfaces · doi:10.1145/2070481.2070509 · Cited by 470
Soft Similarity and Soft Cosine Measure: Similarity of Features in Vector Space Model
Sidorov, G., Gelbukh, A., Gómez-Adorno, H., & Pinto, D. (2014) · Computación y Sistemas · doi:10.13053/cys-18-3-2043 · Cited by 378
Self-citationSentic patterns: Dependency-based rules for concept-level sentiment analysis
Poria, S., Cambria, E., Winterstein, G., & Huang, G.-B. (2014) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.05.005 · Cited by 345
Self-citation3D Constrained Local Model for rigid and non-rigid facial tracking
Baltrusaitis, T., Robinson, P., & Morency, L. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247980 · Cited by 313
Cited by
Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018
Tensor Fusion Network for Multimodal Sentiment Analysis
Zadeh, A., Chen, M., Poria, S., Cambria, E., & Morency, L.-P. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1115 · Cited by 2,102
Self-citationA review of affective computing: From unimodal analysis to multimodal fusion
Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.003 · Cited by 1,644
Self-citationDeep multimodal learning: a survey on recent advances and trends
Ramachandram, D., & Taylor, G. W. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2738401 · Cited by 1,139
MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis
Hazarika, D., Zimmermann, R., & Poria, S. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413678 · Cited by 1,073
“roposed works in this category have primarily focused on learning cross-modal dynamics using sophisticated fusion mechanisms. These works include variety of methods, such as, multiple kernel learning [42], and tensor-based fusion (including its low-rank variants) [14, 21, 26, 29, 31, 58]. While these works perform fus…”
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Metadata
- Authors
- Poria, S., Cambria, E., & Gelbukh, A.
- Venue
- Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
- Published
- 2015
- Pages
- 2539-2544
- OpenAlex
- W2251394420
- Cited by
- 533
- References
- 19
- FWCI
- 32.38
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Poria, S., Cambria, E., & Gelbukh, A. (2015). Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/d15-1303
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