Multimodal Sentiment Analysis Using Deep Learning: A Review
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-59097-9_2
Reference33 articles.
1. Savla, M., Gopani, D., Ghuge, M., Chaudhari, S., Raundale, P.: Sentiment analysis of human speech using deep learning. In: 2023 3rd International Conference on Intelligent Technologies (CONIT), pp. 1–6. IEEE, June 2023
2. Bhat, A., Mahar, R., Punia, R., Srivastava, R.: Exploring multimodal sentiment analysis through cartesian product approach using BERT embeddings and ResNet-50 encodings and comparing performance with pre-existing models. In: 2022 3rd International Conference for Emerging Technology (INCET), pp. 1–6. IEEE, May 2022
3. Rao, A., Ahuja, A., Kansara, S., Patel, V.: Sentiment analysis on user-generated video, audio and text. In: 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), pp. 24–28. IEEE, February 2021
4. Zhu, L., Zhu, Z., Zhang, C., Xu, Y., Kong, X.: Multimodal sentiment analysis based on fusion methods: a survey. Inf. Fusion 95, 306–325 (2023)
5. Agarwal, A., Yadav, A., Vishwakarma, D.K.: Multimodal sentiment analysis via RNN variants. In: 2019 IEEE International Conference on Big Data, Cloud Computing, Data Science and Engineering (BCD), pp. 19–23. IEEE, May 2019
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