Abstract
Sentiment analysis and opinion mining is the field of study to analyse opinions, attitudes, and emotions of people. It is the most studied research field. With the evolution of social media, many new terms have been evolved including selfies. A selfie has provided a way for to the users of social media to record their personal memories. People are sharing their images on social media in a massive amount. Keeping in mind the big data nature, it is very difficult to analyse selfies manually. In this research study, we have proposed a framework called InstaSent for sentiment analysis based on Instagram selfies. This framework incorporates both text mining and image mining techniques for sentiment prediction. Support vector machine is used for sentiment classification based on the text associated with selfies like captions, hashtags, comments, and emoticons, while the deep learning method Convolutional neural network is used for processing image data for sentiment analyses. By combining the practices for text mining and image mining we believe that this technique will outperform all other techniques presented in this domain. In a nutshell, we believe that our research study has provided novel opportunities for researchers to explore the use of selfies in other domains.
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
Bim, A.: The rise and rise of the selfie. The Guardian (London). Accessed 6 April 2013
McHugh, J.: Selfies’ just as much for the insecure as show-offs. Bunbury Mail, 3 April 2013. Accessed 6 Apr 2013
Steinmetz, K.: Top 10 Buzzwords – 9 Selfie, Time, 4 December 2012
Melanie, H.: Family albums fade as the young put only themselves in picture telegraph, 13 June 2013
The Oxford Dictionaries Word of the Year 2013 is OxfordWords blog, 18 November 2013. Blog.oxforddictionaries.com. Accessed 29 Nov 2013
Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)
Nasukawa, T., Yi, J.: Sentiment analysis: capturing favorability using natural language processing. In: Proceedings of the 2nd International Conference on Knowledge Capture. ACM, pp 70–77 (2003)
Yuan, J., You, Q., Luo, J.: Sentiment analysis using social multimedia. In: Multimedia Data Mining and Analytics, pp. 31–59. Springer (2015)
You, Q., Luo, J.: Towards social imagematics: sentiment analysis in social multimedia. In: Proceedings of the Thirteenth International Workshop on Multimedia Data Mining, MDMKDD 2013, pp. 3:1–3:8. ACM, New York (2013)
Wang, Y., et al.: Unsupervised sentiment analysis for social media images. In: Proceedings of the 24th International Conference on Artificial Intelligence. AAAI Press (2015)
Borth, D., et al.: Large-scale visual sentiment ontology and detectors using adjective noun pairs. In: Proceedings of the 21st ACM International Conference on Multimedia. ACM (2013)
Plutchik, R.: The nature of emotions. Am. Sci. 89(4), 344–350 (2001)
Baecchi, C., et al.: A multimodal feature learning approach for sentiment analysis of social network multimedia. Multimedia Tools Appl. 1–19 (2015)
You, Q., et al.: Joint visual-textual sentiment analysis with deep neural networks. In: Proceedings of the 23rd Annual ACM Conference on Multimedia Conference. ACM (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)
Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. arXiv preprint arXiv:1405.4053 (2014)
Katsurai, M., Satoh, S.I.: Image sentiment analysis using latent correlations among visual, textual, and sentiment views (2016)
Ji, R., Cao, D., Lin, D.: Cross-modality sentiment analysis for social multimedia. In: 2015 IEEE International Conference on Multimedia Big Data (BigMM). IEEE (2015)
Kalayeh, M.M., et al.: How to take a good selfie? In: Proceedings of the 23rd Annual ACM Conference on Multimedia Conference. ACM (2015)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Seo, S., Kang, D.: Study on predicting sentiment from images using categorical and sentimental keyword-based image retrieval. J. Supercomput. 1–11 (2015)
Sermanet, P., et al.: Overfeat: integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229 (2013)
http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/
Qamar, U., et al.: A majority vote based classifier ensemble for web service classification. Bus. Inf. Syst. Eng. 1–11
Wang, H., et al.: Web service classification using support vector machine. In: 2010 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), vol. 1. IEEE (2010)
Varguez-Moo, M., Moo-Mena, F., Uc-Cetina, V.: Use of classification algorithms for semantic web services discovery. J. Comput. 8(7), 1810–1814 (2013)
Mullen, T., Nigel, C.: Sentiment analysis using support vector machines with diverse information sources. In: EMNLP, vol. 4 (2004)
Apoorv, A., et al.: Sentiment analysis of twitter data. In: Proceedings of the Workshop on Languages in Social Media. Association for Computational Linguistics (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Noureen, R., Qamar, U., Khan, F.H., Muhammad, I. (2019). InstaSent: A Novel Framework for Sentiment Analysis Based on Instagram Selfies. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-01054-6_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-01054-6_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01053-9
Online ISBN: 978-3-030-01054-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)