Journal of Platform Technology

ICT Platform Society (ICT플랫폼학회)
권호 표지

A study on real-time internet comment system through sentiment analysis and deep learning application

  • Hae-Jong Joo (University of Kangnam, Dept. of KNU Cham-Injae College) ;
  • Ho-Bin Song (University of Mokwon, Dept. of Electrical & Electric Engineering)
  • Received : 2023.03.27
  • Accepted : 2023.04.17
  • Published : 2023.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes a big data sentiment analysis method and deep learning implementation method to provide a webtoon comment analysis web page for convenient comment confirmation and feedback of webtoon writers for the development of the cartoon industry in the video animation field. In order to solve the difficulty of automatic analysis due to the nature of Internet comments and provide various sentiment analysis information, LSTM(Long Short-Term Memory) algorithm, ranking algorithm, and word2vec algorithm are applied in parallel, and actual popular works are used to verify the validity. If the analysis method of this paper is used, it is easy to expand to other domestic and overseas platforms, and it is expected that it can be used in various video animation content fields, not limited to the webtoon field

Keywords

References

  1. 1. Philipp A. Rauschnabel, Reto Felix, Chris Hinsch, Augmented reality marketing: How mobile ARapps can improve brands through inspiration, Journal of Retailing and Consumer Services, Volume 49, pp.43-53, 2019. https://doi.org/10.1016/j.jretconser.2019.03.004
  2. Graphic Nevel Industry White Paper, Korea Creative Content Agency, 2017.
  3. Young-Kyu Kima and Min Ho Ryu, Towards Entrepreneurial Organization: From the case of Organizational Process Innovation in Naver, Procedia Computer Science 122, Information Technology and Quantitative Management (ITQM 2017), pp.663-670. 2017.
  4. Boemer F, Lao Y, Cammarota R, Wierzynski C (2019) nGraph-HE: a graph compiler for deep learning on Homomorphically encrypted data. ACM International Conference on Computing Frontiers 2019:1-27
  5. Nallapati, Ramesh, Zhou, Bowen, dos Santos, Cicero, Gulcehre, Caglar, Xiang, Bing. "Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond." Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, Berlin, Germany, pp.280-290, Aug 2016.
  6. Barret Zoph and Quoc V. Le. Neural Architecture Search with Reinforcement Learning. arXiv eprints, art. arXiv:1611.01578, November 2016.
  7. Vaswani, Ashish, Shazeer, Noam, Parmar, Niki, Uszkoreit, Jakob, Jones, Llion, Gomez, Aidan N., Kaiser, Lukasz, Polosukhin, Illia. "Attention Is All You Need" 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. Dec 2017.
  8. Kunkel, J., Loepp, B., & Ziegler, J.. "A 3D item space visualization for presenting and manipulating user preferences in collaborative filtering." In Proceedings of the 22nd International Conference on Intelligent User Interfaces (pp. 3-15). ACM. March, 2017.
  9. Yann Lecun, Leon Bottou, Yoshua Bengio, Patrick Haffner, "GradientBased Learning Applied to Document Recognition," Proceedings of the IEEE 86.11, 1998.
  10. Mihalcea, Rada. "Graph-based ranking algorithms for sentence extraction, applied to text summarization." In Proceedings of the 42nd Annual Meeting of the Association for Computational Lingusitics(ACL 2004) (companion volume), Barcelona, Spain. 2004.
  11. Yang You, "MIC-SVM: Designing a Highly Efficient Support Vector Machine for Advanced Modern Multi-core and Many-Core Architectures,"2014 IEEE 28th International, 2014.
  12. Dony, Robert D., and Simon Haykin. "Neural network approaches to image compression," Proceedings of the IEEE 83.2, 1995.
  13. Quoc Le, Tomas Milokov, "Distributed Representations of Sentences and Documents," Proceedings of the 31st International Conference on Machine Learning, 2014.
  14. Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean, "Efficient Estimation of word Representations in Vector Space," arXiv:1301.3781, 2013.