Experimental Results

  • Arindam ChaudhuriEmail author
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)


The experimental results are highlighted in this chapter using Twitter, Instagram, Viber and Snapchat datasets. HGFRNN is evaluated through 2-class (+ve, −ve) as well as 3-class (+ve, −ve, unbiased) propositions.


  1. 1.
    Xu, C., Cetintas, S., Lee, K. C., Li, L. J.: Visual sentiment prediction with deep convolutional neural networks. arXiv:1411.5731. (2014)Google Scholar
  2. 2.
    Cao, D., Ji, R., Lin, D., Li, S.: Visual sentiment topic model-based microblog image sentiment analysis. Multimedia Tools Appl. 75(15), 8955–8968 (2016)CrossRefGoogle Scholar
  3. 3.
    Haykin, S.: Neural networks and learning machines, 3rd edn. Prentice Hall of India (2016)Google Scholar
  4. 4.
    Chaudhuri, A.: A journey from neural networks to deep learning networks: some thoughts. Technical Report, TH-7069. Birla Institute of Technology Mesra, Patna Campus (2014)Google Scholar
  5. 5.
    Chung, J., Ahn, S., Bengio, Y.: Hierarchical multiscale recurrent neural networks. arXiv:1609.01704v7. (2017)Google Scholar
  6. 6.
    Open-source implementations of RNN in TensorFlow:
  7. 7.
    Yu, Y., Lin, H., Yu, Q., Meng, J., Zhao, Z., Li, Y., Zuo, L.: Modality classification for medical images using multiple deep convolutional neural networks. J. Comput. Inf. Syst. 11(15), 5403–5413 (2015)Google Scholar

Copyright information

© The Author(s), under exclusive to Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Samsung R & D Institute DelhiNoidaIndia

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