Karray, F.; Alemzadeh, M.; Abou Saleh, J.; Nours Arab, M. Human-Computer Interaction: Overview on State of the Art. Int. J. Smart Sens. Intell. Syst. 2008, 1, 137–159.
Auxier, B.; Anderson, M. Social media use in 2021. Pew Res. Cent. 2021.
Sivarajah, U.; Kamal, M.M.; Irani, Z.; Weerakkody, V. Critical analysis of Big Data challenges and analytical methods. J. Bus. Res. 2017, 70, 263–286.
Bansal, B.; Srivastava, S. On predicting elections with hybrid topic based sentiment analysis of tweets. Procedia Comput. Sci. 2018, 135, 346–353.
El Alaoui, I.; Gahi, Y.; Messoussi, R.; Chaabi, Y.; Todoskoff, A.; Kobi, A. A novel adaptable approach for sentiment analysis on big social data. J. Big Data 2018, 5, 12.
Drus, Z.; Khalid, H. Sentiment Analysis in Social Media and Its Application: Systematic Literature Review. Procedia Comput. Sci. 2019, 161, 707–714.
Zhao, H.; Liu, Z.; Yao, X.; Yang, Q. A machine learning-based sentiment analysis of online product reviews with a novel term weighting and feature selection approach. Inf. Processing Manag. 2021, 58, 102656.
Dashtipour, K.; Gogate, M.; Adeel, A.; Larijani, H.; Hussain, A. Sentiment Analysis of Persian Movie Reviews Using Deep Learning. Entropy 2021, 23, 596.
Farisi, A.A.; Sibaroni, Y.; Faraby, S.A. Sentiment analysis on hotel reviews using Multinomial Naïve Bayes classifier. J. Phys. Conf. Ser. 2019, 1192, 012024.
Melton, C.A.; Olusanya, O.A.; Ammar, N.; Shaban-Nejad, A. Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence. J. Infect. Public Health 2021, 14, S1876034121002288.
Mishra, N.; Jha, C.K. Classification of Opinion Mining Techniques. Int. J. Comput. Appl. 2012, 56, 1–6.
Kim, M.; Lee, S.M.; Choi, S.; Kim, S.Y. Impact of visual information on online consumer review behavior: Evidence from a hotel booking website. J. Retail. Consum. Serv. 2021, 60, 102494.
Xiao, Z.; Wang, L.; Du, J.Y. Improving the Performance of Sentiment Classification on Imbalanced DatasetsWith Transfer Learning. IEEE Access 2019, 7, 28281–28290.
Praveen Gujjar, J.; Prasanna Kumar, H.R.; Chiplunkar, N.N. Image Classification and Prediction using Transfer Learning in Colab Notebook. Glob. Transit. Proc. 2021, 2, S2666285X21000960.
Zhang, Q.; Yang, Q.; Zhang, X.; Bao, Q.; Su, J.; Liu, X.Waste image classification based on transfer learning and convolutional neural network. Waste Manag. 2021, 135, 150–157.
Dilshad, S.; Singh, N.; Atif, M.; Hanif, A.; Yaqub, N.; Farooq, W.A.; Ahmad, H.; Chu, Y.; Masood, M.T. Automated image classification of chest X-rays of COVID-19 using deep transfer learning. Results Phys. 2021, 28, 104529.
Siersdorfer, S.; Minack, E.; Deng, F.; Hare, J. Analyzing and predicting sentiment of images on the social web. In Proceedings of the International Conference on Multimedia MM ’10, Firenze, Italy, 25–29 October 2010; pp. 715–718.
Rao, T.; Xu, M.; Liu, H.; Wang, J.; Burnett, I. Multi-scale blocks based image emotion classification using multiple instance learning. In Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 25–28 September 2016; pp. 634–638.
Datta, R.; Joshi, D.; Li, J.; Wang, J.Z. Studying Aesthetics in Photographic Images Using a Computational Approach. In Computer Vision—ECCV 2006; Leonardis, A., Bischof, H., Pinz, A., Eds.; Springer: Berlin/Heidelberg, Germany, 2006; Volume 3953, pp. 288–301.
Marchesotti, L.; Perronnin, F.; Larlus, D.; Csurka, G. Assessing the aesthetic quality of photographs using generic image In Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain, 6–13 November 2011; pp. 1784–1791.
Borth, D.; Chen, T.; Ji, R.; Chang, S.-F. SentiBank: Large-scale ontology and classifiers for detecting sentiment and emotions in visual content. In Proceedings of the 21st ACM International Conference on Multimedia—M ’13, Barcelona, Spain, 21–25 October 2013; pp. 459–460.
Yuan, J.; Mcdonough, S.; You, Q.; Luo, J. Sentribute: Image sentiment analysis from a mid-level perspective. In Proceedings of the Second InternationalWorkshop on Issues of Sentiment Discovery and Opinion Mining—WISDOM ’13, Chicago, IL, USA, 11 August 2013; pp. 1–8.
Zhao, Z.; Zhu, H.; Xue, Z.; Liu, Z.; Tian, J.; Chua, M.C.H.; Liu, M. An image-text consistency driven multimodal sentiment analysis approach for social media. Inf. Processing Manag. 2019, 56, 102097.
Fernandez, D.; Woodward, A.; Campos, V.; Giro-i-Nieto, X.; Jou, B.; Chang, S.-F. More cat than cute? Interpretable Prediction of Adjective-Noun Pairs. In Proceedings of the Workshop on Multimodal Understanding of Social, Affective and Subjective Attributes, Mountain View, CA, USA, 27 October 2017; pp. 61–69.
Yang, J.; She, D.; Sun, M.; Cheng, M.-M.; Rosin, P.L.; Wang, L. Visual Sentiment Prediction Based on Automatic Discovery of Affective Regions. IEEE Trans. Multimed. 2018, 20, 2513–2525.
Wang, J.; Fu, J.; Xu, Y.; Mei, T. Beyond Object Recognition: Visual Sentiment Analysis with Deep Coupled Adjective and Noun Neural Networks. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16), New York, NY, USA, 9–15 July 2016; pp. 3484–3490.
Song, K.; Yao, T.; Ling, Q.; Mei, T. Boosting image sentiment analysis with visual attention. Neurocomputing 2018, 312, 218–228.
Ortis, A.; Farinella, G.M.; Torrisi, G.; Battiato, S. Visual Sentiment Analysis Based on on Objective Text Description of Images. In Proceedings of the 2018 International Conference on Content-Based Multimedia Indexing (CBMI), La Rochelle, France, 4–6 September 2018; pp. 1–6.
Xu, J.; Huang, F.; Zhang, X.; Wang, S.; Li, C.; Li, Z.; He, Y. Sentiment analysis of social images via hierarchical deep fusion of content and links. Appl. Soft Comput. 2019, 80, 387–399.
Huang, F.; Zhang, X.; Zhao, Z.; Xu, J.; Li, Z. Image-text sentiment analysis via deep multimodal attentive fusion. Knowl. Based Syst. 2019, 167, 26–37.
Deng, J.; Dong, W.; Socher, R.; Ki, L.; Li, K.; Fei-Fei, K. ImageNet: A large-scale hierarchical image database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, FL, USA, 20–25 June 2009.
Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv 2014, arXiv:1409.1556.
He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 770–778.
Usha Kingsly Devi, K., & Gomathi, V. (2023). Deep Convolutional Neural Networks with Transfer Learning for Visual Sentiment Analysis. Neural Processing Letters, 55(4), 5087-5120.
Chandrasekaran, G., Antoanela, N., Andrei, G., Monica, C., & Hemanth, J. (2022). Visual sentiment analysis using deep learning models with social media data. Applied Sciences, 12(3), 1030.
An, J., Zainon, W. M. N. W., & Ding, B. (2023). Leveraging Vision-Language Pre-Trained Model and Contrastive Learning for Enhanced Multimodal Sentiment Analysis. Intelligent Automation & Soft Computing, 37(2).
)