Integrating Multimodal Data For Enhanced Analysis And Understanding: Techniques For Sentiment Analysis And Cross-Modal Retrieval
Main Article Content
Abstract
In today's dynamic digital landscape, the prevalence of multimedia content across various platforms underscores the importance of advanced techniques for analyzing data across diverse modalities. This paper explores the integration of text data with other modalities such as images, videos, and audio to enable comprehensive analysis and understanding. Specifically, the focus is on investigating methods for sentiment analysis in multimedia content and facilitating cross-modal retrieval. The paper addresses the challenges and opportunities in multimodal analysis, reviews existing techniques, and proposes novel methods for enhancing sentiment analysis and cross-modal retrieval through multimodal fusion and deep learning architectures. The challenges inherent in multimodal analysis include data heterogeneity, semantic gap, modality imbalance, and scalability. These challenges necessitate the development of robust techniques for multimodal fusion, feature representation, and cross-modal mapping. Existing methods, including early fusion, late fusion, and hybrid fusion techniques, are reviewed, alongside recent advancements in deep learning-based multimodal fusion architectures. Proposed methodologies aim to augment sentiment analysis and cross-modal retrieval through innovative multimodal fusion techniques and deep learning architectures. Experimental evaluations validate the effectiveness of the proposed methods in improving sentiment analysis accuracy and cross-modal retrieval performance. This research contributes to advancing techniques for analyzing and understanding multimedia content in the increasingly complex digital landscape, facilitating enhanced data-driven insights and decision-making processes across various domains.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Baltrušaitis, T., Ahuja, C., & Morency, L. P. (2019). Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 423-443.
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 248-255). IEEE.
Li, Y., Wang, Y., & Zhang, C. (2018). Cross-modal retrieval with a generative adversarial network. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 1663-1672).
Peng, X., & Natarajan, P. (2015). Cross-media learning to rank with collective matrix factorization. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 115-124).
Poria, S., Cambria, E., Hazarika, D., & Vij, P. (2017). A review of affective computing: From unimodal analysis to multimodal fusion. Information Fusion, 37, 98-125.
Socher, R., Huval, B., Manning, C. D., & Ng, A. (2012). Semantic compositionality through recursive matrix-vector spaces. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (pp. 1201-1211).
Wang, J., Yang, J., Mao, J., Huang, Z., Huang, C., & Xu, W. (2016). CNN-RNN: A unified framework for multi-label image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2285-2294).
Zhang, X., Zhao, J., & LeCun, Y. (2015). Character-level convolutional networks for text classification. In Advances in Neural Information Processing Systems (pp. 649-657).
Zhou, Y., Cui, P., Liu, S., Wang, M., & Yang, S. (2018). Graph neural networks: A review of methods and applications. arXiv preprint arXiv:1812.08434.
Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., & Fidler, S. (2015). Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. In Proceedings of the IEEE International Conference on Computer Vision (pp. 19-27).