Image-Based Sentiment Analysis Using InceptionV3 Transfer Learning Approach

被引：0
作者：
Meena G. [1 ]
Mohbey K.K. [1 ]
Kumar S. [1 ,3 ]
Chawda R.K. [2 ]
Gaikwad S.V. [4 ]
机构：
[1] Department of Computer Science, Central University of Rajasthan, Ajmer
[2] Department of Computer Science, Assam University, Silchar
[3] School of Business, Woxsen University, Hyderabad
[4] Faculty of Computer Science and Application, SMPICA, Charotar University of Science and Technology, Gujrat, Anand
来源：
SN Computer Science | / 4卷 / 3期
关键词：
Artificial Intelligence; Deep learning; Image sentiment analysis; InceptionV3; Transfer learning;
D O I：
10.1007/s42979-023-01695-3
中图分类号：
学科分类号：
摘要：
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. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
引用
收藏
相关论文
共 31 条
  • [21] Mohbey K.K., Multi-class approach for user behavior prediction using deep learning framework on twitter election dataset, J Data Inform Manage, 2, 1, pp. 1-14, (2020)
  • [22] Bansal M., Kumar M., Sachdeva M., Mittal A., Transfer learning for image classification using VGG19: caltech-101 image data set, J Am Intell Human Compt, (2021)
  • [23] Albu F., Hagiescu D., Vladutu L., Puica M.A., Neural network approaches for children’s emotion recognition in intelligent learning applications, In: EDULEARN15 7Th Annu Int Conf Educ New Learn Technol. 6Th-8Th. Barcelona. Spain, (2015)
  • [24] Simonyan K., Zisserman A., Very Deep Convolutional Networks for Large-Scale Image Recognition. Arxiv Preprint Arxiv, 1409, (2014)
  • [25] Ahmed S., Shaikh A., Alshahrani H., Alghamdi A., Alrizq M., Baber J., Bakhtyar M., Transfer learning approach for classification of histopathology whole slide images, Sensors, 21, 16, (2021)
  • [26] Shaees S., Naeem H., Arslan M., Naeem M.R., Ali S.H., Aldabbas H., ) Facial emotion recognition using transfer learning, In: 2020 International Conference on Computing and Information Technology (ICCIT-1441)., pp. 1-5, (2020)
  • [27] Szegedy C., Ioffe S., Vanhoucke V., Alemi A.A., Inception-v4, inception-resnet and the impact of residual connections on learning, Thirty-first AAAI Conf Artificial Intell, (2017)
  • [28] Yang L., Zhang H., Li D., Xiao F., Yang S., Facial expression recognition based on transfer learning and SVM, J Phys Conf Ser., 2025, 1, (2021)
  • [29] Babu N.V., Kanaga E., Sentiment analysis in social media data for depression detection using artificial intelligence: a review, SN Compt Sci, 3, 1, pp. 1-20, (2022)
  • [30] Chakraborty S., Paul S., Hasan K.M., A transfer learning-based approach with deep cnn for covid-19-and pneumonia-affected chest x-ray image classification, SN Compt Sci, 3, 1, pp. 1-10, (2022)
1234