Abstract
There has been a recent shift from using text-based sentiment analysis in favor of an image-based method. In recent years, transfer learning methods have been widely utilized in developing a comprehensive image sentiment analysis approach. Deep learning algorithms have produced remarkable outcomes in a variety of contexts. Image-based sentiment analysis presents many difficulties, but there also appears to be much space for development. A significant improvement over prior work is provided by an InceptionV3 approach that can easily focus on huge body portions like a human face. This research improves image categorization performance with InceptionV3, a popular deep convolutional neural network, and other deep features. Using a Convolutional Neural Network based on InceptionV3 architecture, we identify and classify emotions using the famous CK + , FER2013, and JAFFE datasets. Experiments reveal that the proposed model achieves 99.5% accuracy on the CK + dataset. Also, the accuracies of JAFFE and FER2013 are 86% and 73%, respectively. A person’s emotional patterns and mental health can be evaluated using this approach.
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18 May 2023
A Correction to this paper has been published: https://doi.org/10.1007/s42979-023-01874-2
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Meena, G., Mohbey, K.K., Kumar, S. et al. Image-Based Sentiment Analysis Using InceptionV3 Transfer Learning Approach. SN COMPUT. SCI. 4, 242 (2023). https://doi.org/10.1007/s42979-023-01695-3
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DOI: https://doi.org/10.1007/s42979-023-01695-3