Deep Learning-Based Ensemble Method for Sentiment Analysis on Images
Corressponding author's email:
trangpth@hcmute.edu.vnDOI:
https://doi.org/10.54644/jte.2024.1547Keywords:
Image sentiment analysis, Ensemble model, VGG19-based CNN, ResNet50-based CNN, Convolutional neural networkAbstract
Sentiment analysis is to identify the polarity of people's emotions toward entities as expressed in their opinions. With the development of science and technology, opinions published on social networks become more diverse in forms, including texts, images, sounds, and videos. Among them, opinions expressed through images increasingly dominate. Many image sentiment analysis methods have been published in recent years. Methods based on convolutional neural networks (CNNs) are notable. However, previous methods based on CNNs often cannot extract features well from low-resolution images. To solve the mentioned problem, in this study, we propose a method to improve the performance of sentiment analysis on images by combining two transfer learning models and a CNN model. Unlike other CNN-based models, our method can better extract local and global features on images. The proposed method was experimented on the FER2013 dataset and shown that it can improve accuracy by up to 9.03% compared to baseline methods.
Downloads: 0
References
G. Meena, K. K. Mohbey and S. Kumar, "Sentiment analysis on images using convolutional neural networks based Inception-V3 transfer learning approach," International journal of information management data insights, p. 100174, 2023. DOI: https://doi.org/10.1016/j.jjimei.2023.100174
E. Pranav, S. Kamal, C. S. Chandran and M. Supriya, "Facial emotion recognition using deep convolutional neural network," in 6th International conference on advanced computing and communication Systems (ICACCS), IEEE, 2020, pp. 317-320. DOI: https://doi.org/10.1109/ICACCS48705.2020.9074302
N. Mehendale, "Facial emotion recognition using convolutional neural networks (FERC)," SN Applied Sciences, vol. 2, no. 3, p. 446, 2020. DOI: https://doi.org/10.1007/s42452-020-2234-1
A. Jaiswal, A. K. Raju and S. Deb, "Facial emotion detection using deep learning," in International conference for emerging technology (INCET), IEEE, 2020, pp. 1-5. DOI: https://doi.org/10.1109/INCET49848.2020.9154121
S. Modi and M. H. Bohara, "Facial emotion recognition using convolution neural network," in 5th international conference on intelligent computing and control systems (ICICCS), IEEE, 2021, pp. 1339-1344. DOI: https://doi.org/10.1109/ICICCS51141.2021.9432156
K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
A. Agrawal and N. Mittal, "Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy," The Visual Computer, vol. 36, no. 2, pp. 405-412, 2020. DOI: https://doi.org/10.1007/s00371-019-01630-9
K. Liu, M. Zhang, and Z. Pan, "Facial expression recognition with CNN ensemble," in International conference on cyberworlds (CW), IEEE, 2016, pp. 163-166. DOI: https://doi.org/10.1109/CW.2016.34
I. J. Goodfellow et al., "Challenges in representation learning: A report on three machine learning contests," in Neural Information Processing: 20th International Conference, ICONIP, Daegu, Korea, Springer, 2013, pp. 117-124.
K. Gadzicki, R. Khamsehashari and C. Zetzsche, "Early vs late fusion in multimodal convolutional neural networks," in IEEE 23rd international conference on information fusion (FUSION), IEEE, 2020, pp. 1-6. DOI: https://doi.org/10.23919/FUSION45008.2020.9190246
E. G. Moung, C. C. Wooi, M. M. Sufian, C. K. On, and J. A. Dargham, "Ensemble-based face expression recognition approach for image sentiment analysis," Int. J. Electr. Comput. Eng, vol. 12, no. 3, pp. 2588-2600, 2022. DOI: https://doi.org/10.11591/ijece.v12i3.pp2588-2600
C. Jia, C. L. Li, and Z. Ying, "Facial expression recognition based on the ensemble learning of CNNs," in IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), IEEE, 2020, pp. 1-5. DOI: https://doi.org/10.1109/ICSPCC50002.2020.9259543
G. Shengtao, X. Chao, and F. Bo, "Facial expression recognition based on global and local feature fusion with CNNs," in IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), IEEE, 2019, pp. 1-5. DOI: https://doi.org/10.1109/ICSPCC46631.2019.8960765
V. Jha, P. D. Shenoy, and K. Venugopal, "Development of facial expression classifier using neural networks," in IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), IEEE, 2019, pp. 1-4. DOI: https://doi.org/10.1109/WIECON-ECE48653.2019.9019937
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2024 Journal of Technical Education Science
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright © JTE.
