Abstract
Determining the image sentiment is a tedious task for classification algorithms, owing to complexities in the raw images as well as the intangible nature of human sentiments. Classifying image sentiments is an evergreen research area, especially in social data analytics. In current times, it is a common practice for majority people to precise their feelings on the web by substituting text with the upload of images via a multiplicity of social media sites like Facebook, Instagram, Twitter as well as any other platform. To identify the emotions from visual cues, some visual features as well as image processing techniques are used. Several existing systems have already introduced emotion detection using machine learning techniques, but the traditional feature extraction strategies do not achieve the required accuracy on random objects. In the entire process, normalization of image, feature extraction, and feature selection are important tasks in the train module. This work articulates the newest developments in the field of image sentiment employing deep learning techniques. Also, the use of conventional machine learning techniques is compared along with deep learning algorithms. It is indicative that a combination of fast recurrent neural networks and CNN may produce high accuracy with minimum time complexity. It is noted from the survey that existing researchers reflect CNN provides around 96.50% average accuracy for sentiment classification on the flicker image dataset.
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References
Jindal S, Singh S (2015) Image sentiment analysis using deep convolutional neural networks with domain specific fine tuning. In: International conference on information processing (ICIP). IEEE, pp 447–451
Kunte A, Panicker S (2019) Using textual data for personality prediction: a machine learning approach. In: International conference on information systems and computer networks (ISCON), Mathura, India
Kunte A, Panicker S (2019) Personality prediction of social network users using ensemble and XGBoost classifiers. In: 2nd international conference on computing analytics and networking (ICCAN), Bhubhaneshwar
Mittal N, Sharma D, Joshi ML (2018) Image sentiment analysis using deep learning. In: International conference on web intelligence (WI). IEEE, pp 684–687
Kumar A, Jaiswal A (2017) Image sentiment analysis using convolutional neural network. In: International conference on intelligent systems design and applications (ICISDA). Springer, pp 464–473
Wang Y, Hu Y, Kambhampati S, Li B (2015) Inferring sentiment from web images with joint inference on visual and social cues: a regulated matrix factorization approach. In: International conference on web and social media (ICWSM)
Yuhai Y, Hongfei L, Meng J, Zhao Z (2016) Visual and textual sentiment analysis of a microblog using deep convolutional neural networks. Algorithms 9:2
You Q, Luo J, Jin H, Yang J (2015) Robust image sentiment analysis using progressively trained and domain transferred deep networks. In: Twenty-ninth AAAI conference on artificial intelligence
Yang Y, Jia J, Zhang S, Wu B, Chen Q, Li J, Tang J (2014) How do your friends on social media disclose your emotions? In: Proceedings of AAAI conference on artificial intelligence AAAI
Frome A, Corrado GS, Shlens J, Bengio S, Dean J, Ranzato MA, Mikolov T (2013) DeViSE: a deep visual-semantic embedding model. In: Proceedings of advances in neural information processing systems (NIPS), pp 2121–2129
Wang Y, Li B (2015) Sentiment analysis for social media images. In: International conference on data mining workshop (ICDMW). IEEE, pp 1584–1591
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
Mandhyani J, Khatri L, Ludhrani V, Nagdev R, Sahu S (2017) Image sentiment analysis. Int J Eng Sci
https://www.kaggle.com/hsankesara/flickr-image-dataset. Last accessed 29 Nov 2019
https://www.kaggle.com/paoloripamonti/twitter-sentiment-analysis. Last accessed 29 Nov 2019
Ragusa E, Cambria E, Zunino R, Gastaldo P (2019) A survey on deep learning in image polarity detection: balancing generalization performances and computational costs. Electronics 8:783
Gajarla V, Gupta A (2015) Emotion detection and sentiment analysis of images. Institute of Technology, Georgia
Islam J, Zhang Y (2016) Visual sentiment analysis for social images using transfer learning approach. In: International conferences on big data and cloud computing, social computing and networking, sustainable computing and communications (SustainCom) (BDCloud-SocialCom-SustainCom). IEEE, pp 124–130
Anjaria M, Guddeti RMR (2014) Influence factor based opinion mining of Twitter data using supervised learning. In: Sixth international conference on communication systems and networks (COMSNETS). IEEE, pp 1–8
Bhagya C, Shyna A (2019) An overview of deep learning based object detection techniques. In: 1st international conference on innovations in information and communication technology (ICIICT). IEEE, pp 1–6
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Salunke, V., Panicker, S.S. (2021). Image Sentiment Analysis Using Deep Learning. In: Ranganathan, G., Chen, J., Rocha, Á. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 145. Springer, Singapore. https://doi.org/10.1007/978-981-15-7345-3_12
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DOI: https://doi.org/10.1007/978-981-15-7345-3_12
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