The Development of the Sentiment and Visual Characterization Analyzer for the Social Media Images
Keywords:
Deep Learning, Image Analytics, Social Network, HashtagAbstract
The consideration of Hashtag or choosing it appropriately to pictures on social media is one of a good strategy for online marketing. However, it is quite complicated to consider for a proper hashtag because it only requires the experience of online marketers regardless of the pictures under the hashtag. This research is made to generate a tool for analysis of pictures under a searched hashtag or pictures of influencers on social media. Moreover, it is used for making decision on the hashtag and be able to integrate in searching for the suitable influencers on social media.
References
Y. Hu, L. Manikonda and S. Kambhampati, “What We Instagram: A First Analysis of Instagram Photo Content and User Types,” Proceedings of the 8th International Conference on Weblogs and Social Media, The AAAI Press, pp.595-598, 2014.
S. Ibba, M. Orrù, F.E. Pani and S. Porru, “Hashtag of Instagram: From Folksonomy to Complex Network,” Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Lisbon, Portugal, pp.279-284, 2015.
I. Dorsch, “Content Description on a Mobile Image Sharing Service: Hashtags on Instagram,” Journal of Information Science Theory and Practice, vol.6, no.2, pp.46-61, 2018.
A. Mathes, “Folksonomies - cooperative classification and communication through shared metadata,” Journal of Computer-Mediated Communication 47, Oxford University Press, 2004.
Bruns, Axel & Burgess and Jean, “The use of Twitter hashtags in the formation of ad hoc publics,” Proceedings of the 6th European Consortium for Political Research (ECPR) General Conference 2011, The European Consortium for Political Research (ECPR), United Kingdom, pp. 1-9, 2011.
K.S. Satoh, “Image Sentiment Analysis Using Correlations Among Visual, Textual, and Sentiment Views,” International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2016.
V. Gajarla and A. Gupta, “Emotion Detection and Sentiment Analysis of Images,” Georgia Institute of Technology, Atlanta, Georgia, 2011.
S. Karayev, M. Trentacoste, H. Han, A. Agarwala, Trevor Darrell, A. Hertzmann and H. Winnemoeller, “Recognizing Image Style,” In Proceedings British Machine Vision Conference, BMVA Press, 2014.
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva and A. Torralba, “Places: A 10 million Image Database for Scene Recognition,” Transactions on Pattern Analysis and Machine Intelligence. IEEE, vol.40, no.6, pp. 1452 - 1464, 2017.
M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” The International Conference on Machine Learning, Long Beach, California, pp.6105-6114, 2019.
M. Hussain, J. J. Bird and D. R. Faria, “A Study on CNN Transfer Learning for Image Classification,” EventUKCI'18: 18th Annual UK Workshop on Computational Intelligence - Nottingham, United Kingdom, pp.191-202, 2018.
J. Redmon, S. Divvalay, R. Girshick and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA. IEEE, 2016.
T. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C.L. Zitnick and P. Dollár, “Microsoft COCO: Common Objects in Context,” European Conference on Computer Vision, Computer Vision – ECCV 2014, pp. 740-755, 2014.
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