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A NLP Framework to Generate Video from Positive Comments in Youtube

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 451)


Video-sharing sites platforms like YouTube have unique architecture and atmosphere. The comments section is one of the evolution that attracted the users towards expressing opinions and sharing more about videos. Opinions can be used to examine knowledge, user behavior analysis and provide the creator with more ideas to create videos. This paper proposed a novel NLP framework to examine user comments on YouTube and use sentiment analysis to create a short video from positive comments. The results of this study suggest that the framework could be effective to promote the original video using classified community comments. In addition, the results of our implementation indicate that such as framework can be integrated to detect some comments on YouTube and remove negative comments before even posting them.


  • NLP
  • Short-video
  • Automation
  • Video generation
  • Sentiment analysis

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  • DOI: 10.1007/978-3-030-99619-2_19
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Fig. 1.


  1. How Many People Use YouTube in 2021? (New Data), January 2021.

  2. Jones, K.S.: What is the role of NLP in text retrieval? In: Strzalkowski, T. (eds.) Natural Language Information Retrieval. TLTB, vol. 7, pp. 1–24. Springer, Dordrecht (1999).

  3. Perkins, J.: Python Text Processing with NLTK 2.0 Cookbook. Packt Publishing Ltd. (2010)

    Google Scholar 

  4. Jelodar, H., et al.: A NLP framework based on meaningful latent-topic detection and sentiment analysis via fuzzy lattice reasoning on YouTube comments. Multimedia Tools Appl. 80(3), 4155–4181 (2021).

  5. Das, S., Dutta, A., Lindheimer, T., Jalayer, M., Elgart, Z.: YouTube as a source of information in understanding autonomous vehicle consumers: natural language processing study. Transp. Res. Rec. J. Transp. Res. Board 2673(8), 242–253 (2019).

  6. Choudhury, S., Breslin, J.G.: User sentiment detection: a YouTube use case (2010)

    Google Scholar 

  7. Cheng, X., Dale, C., Liu, J.: Understanding the characteristics of internet short video sharing: YouTube as a case study. arXiv preprint arXiv:0707.3670 (2007)

  8. Siersdorfer, S., Chelaru, S., Nejdl, W., San Pedro, J.: How useful are your comments? Analyzing and predicting YouTube comments and comment ratings. In: Proceedings of the 19th International Conference on World Wide Web, pp. 891–900 (2010)

    Google Scholar 

  9. Lynn, T., Endo, P.T., Rosati, P., Silva, I., Santos, G.L., Ging, D.: A comparison of machine learning approaches for detecting misogynistic speech in urban dictionary. In: 2019 International Conference on Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA), pp. 1–8. IEEE (2019)

    Google Scholar 

  10. Gundecha, U.: Learning Selenium Testing Tools with Python. Packt Publishing Ltd. (2014)

    Google Scholar 

  11. Doberkat, E.-E.: 11. Einfache video-manipulation. In: Python 3. De Gruyter Oldenbourg, pp. 213–224 (2018)

    Google Scholar 

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Correspondence to Hamza Salem .

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Salem, H., Mazzara, M. (2022). A NLP Framework to Generate Video from Positive Comments in Youtube. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 451. Springer, Cham.

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