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Research Article | Open Access |

Detection of Visual Positive Sentiment using PCNN

Author 1: Samar H. Ahmed Author 2: Emad Nabil Author 3: Amr A. Badr
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 10, No. 1 · Published 2019

DOI: https://doi.org/10.14569/IJACSA.2019.0100134

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Abstract

Many people all over the world use online social networks to express their feeling and sharing their experience, and the easiest way from their perspective is using images and videos to do so. This paper shows the utilization of two techniques (Viola et al algorithm and Pulse coupled Neural Network) in visual sentiment analysis using a hand-labeled dataset. The proposed system, which uses the PCNN with NN classifier, achieves 96% right classification, whereas Viola algorithm achieves 94% for the same dataset.

Keywords

Visual sentiment analysis pulse coupled neural network (PCNN) viola et al. algorithm

How to Cite this Article

Ahmed, S. H., Nabil, E., & Badr, A. A. (2019). Detection of Visual Positive Sentiment using PCNN. International Journal of Advanced Computer Science and Applications, 10(1). https://doi.org/10.14569/IJACSA.2019.0100134

Ahmed, Samar H., et al.. "Detection of Visual Positive Sentiment using PCNN." International Journal of Advanced Computer Science and Applications, vol. 10, no. 1, 2019, https://doi.org/10.14569/IJACSA.2019.0100134.

@article{Ahmed2019,
  title     = {Detection of Visual Positive Sentiment using PCNN},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {1},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Samar H. Ahmed and Emad Nabil and Amr A. Badr},
  doi       = {10.14569/IJACSA.2019.0100134},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100134}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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