Abstract
Visual sentiment analysis seeks to comprehend the emotional responses that images elicit in viewers. Despite the fact that this topic is relatively new, a wide variety of strategies have been created for diverse data sources and issues, leading to a substantial body of study. This Thesis attempts to provide a thorough overview of the subject by reviewing key literature. The topic is covered under many primary headings after a description of the task and the relevant applications. The article describes the design principles of broad Visual Sentiment Analysis systems from the three main perspectives of emotional models, dataset specification, and feature design. Recent Deep FER systems have typically concentrated on two main problems: overfitting brought on by a lack of training data and expression-unrelated factors including illumination, head posture, and identification bias. Authors present a full evaluation of Deep FER in this study. This study presents details on FER dataset with DEEP Learning model i.e. CNN and comparative analysis with other models such as SVM, K-Means. Convolutional neural networks (CNNs), in particular, have among all FER techniques demonstrated enormous potential because to their robust automated feature extraction and computational efficiency. In this work, we achieve the highest classification accuracy on the FER2013 dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aggarwal T, Sharma N, Aggarwal N (2023) Gunshot detection and classification using a convolution-GRU based approach. In: Noor A, Saroha K, Pricop E, Sen A, Trivedi G (eds) Proceedings of emerging trends and technologies on intelligent systems. Advances in intelligent systems and computing, vol 1414. Springer, Singapore. https://doi.org/10.1007/978-981-19-4182-5_8
Al-Halah Z, Aitken A, Shi W, Caballero J (2019) Smile, be happy:) emoji embedding for visual sentiment analysis. In: Proceedings of the IEEE/CVF international conference on computer vision workshops, pp 0–0
Corchs S, Fersini E, Gasparini F (2019) Ensemble learning on visual and textual data for social image emotion classification. Int J Mach Learn Cybern 10(8):2057–2070. Springer Science and Business Media LLC
Ding H, Zhou SK, Chellappa R (2017) Facenet2expnet: regularizing a deep face recognition net for expression recognition. In: 2017 12th IEEE international conference on automatic face and gesture recognition (FG 2017). IEEE, pp 118–126
Gonçalves P, Araújo M, Benevenuto F, Cha M (2013) Comparing and combining sentiment analysis methods. In: Proceedings of the first ACM conference on online social networks—COSN ’13. ACM Press
Hamester D, Barros P, Wermter S (2015) Face expression recognition with a 2-channel convolutional neural network. In: 2015 International joint conference on neural networks (IJCNN). IEEE, pp 1–8
Hasan A, Moin S, Karim A, Shamshirband S (2018) Machine learning-based sentiment analysis for twitter accounts. Math Comput Appl 23(1):11. MDPI AG
Kaya M, Fidan G, Toroslu IH (2012) Sentiment analysis of Turkish political news. In: 2012 IEEE/WIC/ACM international conferences on web intelligence and intelligent agent technology. IEEE
Khorrami P, Paine T, Huang T (2015) Do deep neural networks learn facial action units when doing expression recognition? arXiv preprint arXiv:1510.02969v3
Kumar A, Srinivasan K, Cheng W-H, Zomaya AY (2020) Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data. Inf Process Manag 57(1):102141. Elsevier BV
Liu X, Kumar BV, Jia P, You J (2019) Hard negative generation for identity-disentangled facial expression recognition. Pattern Recognit 88:1–12
Liu X, Kumar B, You J, Jia P (2017) Adaptive deep metric learning for identity-aware facial expression recognition. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops (CVPRW), pp 522–531
Machajdik J, Hanbury A (2010) Affective image classification using features inspired by psychology and art theory. In: Proceedings of the international conference on multimedia—MM ’10. ACM Press
Meng Z, Liu P, Cai J, Han S, Tong Y (2017) Identity-aware convolutional neural network for facial expression recognition. In: 2017 12th IEEE international conference on automatic face and gesture recognition (FG 2017). IEEE, pp 558–565
Ortis A, Farinella GM, Battiato S (2020) Survey on visual sentiment analysis. IET Image Process 14(8):1440–1456. Institution of Engineering and Technology (IET)
Ortis A, Farinella GM, Torrisi G, Battiato S (2021) Exploiting objective text description of images for visual sentiment analysis. Multimed Tools Appl 80(15):22323–22346. Springer Science and Business Media LLC
Pall A, Sharma N, Sharma K, Wadhwa V (2022) A systematic review of deep learning techniques for semantic image segmentation: methods, future directions, and challenges. In: Handbook of research on machine learning
Priyavrat SN, Sikka G (2021) Multimodal sentiment analysis of social media data: a review. In: Singh PK, Singh Y, Kolekar MH, Kar AK, Chhabra JK, Sen A (eds) Recent innovations in computing. ICRIC 2020. Lecture notes in electrical engineering, vol 701. Springer, Singapore. https://doi.org/10.1007/978-981-15-8297-4_44
Sharma R, Sharma N (2021) Application of machine learning in precision agriculture. In: Mangla M, Satpathy S, Nayak B, Mohanty SN (eds) Integration of cloud computing with internet of things. https://doi.org/10.1002/9781119769323.ch8
Song K, Yao T, Ling Q, Mei T (2018) Boosting image sentiment analysis with visual attention. Neurocomputing 312:218–228. Elsevier BV
Sun M, Yang J, Wang K, Shen H (2016) Discovering affective regions in deep convolutional neural networks for visual sentiment prediction. In: 2016 IEEE international conference on multimedia and expo (ICME). IEEE
Vadicamo L, Carrara F, Cimino A, Cresci S, Dell’Orletta F, Falchi F, Tesconi M (2017) Cross-media learning for image sentiment analysis in the wild. In: Proceedings of the IEEE international conference on computer vision workshops, pp 308–317
Yang H, Ciftci U, Yin L (2018) Facial expression recognition by de-expression residue learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2168–2177
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Tiwari, K.P.S., Sharma, N., Vats, P., Rakhra, M., Sharma, D. (2024). A Deep Learning Model for Visual Sentiment Analysis of Social Media. In: Sharma, N., Mangla, M., Shinde, S.K. (eds) Big Data Analytics in Intelligent IoT and Cyber-Physical Systems. Transactions on Computer Systems and Networks. Springer, Singapore. https://doi.org/10.1007/978-981-99-4518-4_15
Download citation
DOI: https://doi.org/10.1007/978-981-99-4518-4_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-4517-7
Online ISBN: 978-981-99-4518-4
eBook Packages: EngineeringEngineering (R0)