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Multimedia Tools and Applications

, Volume 78, Issue 2, pp 2465–2479 | Cite as

Automatic point of interest detection for open online educational video lectures

  • Dimitrios KravvarisEmail author
  • Katia Lida Kermanidis
Article
  • 43 Downloads

Abstract

The rise of massive open online courses had as a result an increase in the number of open online educational video lectures on the web, as well as in the number of users who watch them. The present work aims to optimize the searching time within an educational video lecture based on the users’ opinion. The research presents a novel procedure for the automatic point of interest detection in an educational video lecture. State of the art algorithms are used to extract terminology from the users’ comments and video lectures topics from relevant video transcripts. The topics of each video lecture are assessed based on the terminology resulting from the users’ relevant comments. The topic with the best score (adding the keyness value of common topic-related words and terminology words) is selected as the most relevant to the video lecture. Then videos’ timestamps that include the selected topic’s words are presented to users as the points of interest of the video lecture. Finally, a user evaluation experiment is carried out, the results of which strengthen the reliability of the proposed procedure.

Keywords

Video lecture Users’ comments Maximum likelihood Latent Dirichlet allocation Points of interest 

Notes

References

  1. 1.
    Alharbi G, Hain T (2015) Using topic segmentation models for the automatic organisation of moocs resources. In: EDM, pp 524–527Google Scholar
  2. 2.
    Anthony L (2014) Antconc (version 3.4.3)Google Scholar
  3. 3.
    Biswas A, Gandhi A, Deshmukh O (2015) Mmtoc: A multimodal method for table of content creation in educational videos. In: Proceedings of the 23rd ACM international conference on multimedia, pp 621–630. ACMGoogle Scholar
  4. 4.
    Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022zbMATHGoogle Scholar
  5. 5.
    Cha M, Kwak H, Rodriguez P, Ahn YY, Moon S (2007) I tube, you tube, everybody tubes: analyzing the world’s largest user generated content video system. In: Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, pp 1–14. ACMGoogle Scholar
  6. 6.
    Chorianopoulos K (2013) Collective intelligence within web video. Human-Centric Comput Inf Sci 3(1):10CrossRefGoogle Scholar
  7. 7.
    Deldjoo Y, Elahi M, Cremonesi P, Garzotto F, Piazzolla P, Quadrana M (2016) Content-based video recommendation system based on stylistic visual features. J Data Semant 5(2):99–113CrossRefGoogle Scholar
  8. 8.
    Gabrielatos C, Marchi A (2012) Keyness: Appropriate metrics and practical issues. In: CADS International Conference. Available at http://repository.edgehill.ac.uk/4196/1/Gabrielatos (accessed 10 November 2017)
  9. 9.
    Gelbukh A, Sidorov G, Lavin-Villa E, Chanona-Hernandez L (2010) Automatic term extraction using log-likelihood based comparison with general reference corpus. In: International conference on application of natural language to information systems, pp 248–255. SpringerGoogle Scholar
  10. 10.
    Giannakopoulos T, Makris A, Kosmopoulos D, Perantonis S, Theodoridis S (2010) Audio-visual fusion for detecting violent scenes in videos. In: Hellenic conference on artificial intelligence, pp 91–100. SpringerGoogle Scholar
  11. 11.
    Hofmann M, Klinkenberg R (2013) RapidMiner: Data mining use cases and business analytics applications. CRC PressGoogle Scholar
  12. 12.
    Hoic-Bozic N, Dlab MH, Mornar V (2016) Recommender system and web 2.0 tools to enhance a blended learning model. IEEE Trans Educ 59(1):39–44CrossRefGoogle Scholar
  13. 13.
    Imran H, Belghis-Zadeh M, Chang TW, Graf S, et al. (2016) Plors: a personalized learning object recommender system. Vietnam J Comput Sci 3(1):3–13CrossRefGoogle Scholar
  14. 14.
    Ji X, Liu H (2010) Advances in view-invariant human motion analysis: a review. IEEE Trans Syst Man Cybern Part C Appl Rev 40(1):13–24Google Scholar
  15. 15.
    Liu B, Liu L, Tsykin A, Goodall GJ, Green JE, Zhu M, Kim CH, Li J (2010) Identifying functional mirna–mrna regulatory modules with correspondence latent dirichlet allocation. Bioinformatics 26(24):3105–3111CrossRefGoogle Scholar
  16. 16.
    Mansoorizadeh M, Charkari NM (2010) Multimodal information fusion application to human emotion recognition from face and speech. Multimed Tools Appl 49(2):277–297CrossRefGoogle Scholar
  17. 17.
    McCallum AK (2002) Mallet: A machine learning for language toolkitGoogle Scholar
  18. 18.
    Park D, Ramanan D, Fowlkes C (2010) Multiresolution models for object detection. In: European conference on computer vision, pp 241–254. SpringerGoogle Scholar
  19. 19.
    Pazienza MT, Pennacchiotti M, Zanzotto FM (2005) Terminology extraction: an analysis of linguistic and statistical approaches. In: Knowledge mining, pp 255–279. SpringerGoogle Scholar
  20. 20.
    Philbin J, Sivic J, Zisserman A (2011) Geometric latent dirichlet allocation on a matching graph for large-scale image datasets. Int J Comput Vis 95(2):138–153MathSciNetCrossRefGoogle Scholar
  21. 21.
    Pojanapunya P, Todd RW (2016) Log-likelihood and odds ratio: Keyness statistics for different purposes of keyword analysis. Corpus Linguistics and Linguistic TheoryGoogle Scholar
  22. 22.
    Rayson P, Garside R (2000) Comparing corpora using frequency profiling. In: Proceedings of the workshop on Comparing Corpora, pp 1–6. Association for Computational LinguisticsGoogle Scholar
  23. 23.
    Rosen-Zvi M, Griffiths T, Steyvers M, Smyth P (2004) The author-topic model for authors and documents. In: Proceedings of the 20th conference on Uncertainty in artificial intelligence, pp 487–494. AUAI PressGoogle Scholar
  24. 24.
    Rousseau A, Deléglise P, Esteve Y (2012) Ted-lium: an automatic speech recognition dedicated corpus. In: LREC, pp 125–129Google Scholar
  25. 25.
    Wei X, Croft WB (2006) Lda-based document models for ad-hoc retrieval. In: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pp 178–185. ACMGoogle Scholar
  26. 26.
    Willett P (2006) The porter stemming algorithm: then and now. Program 40 (3):219–223CrossRefGoogle Scholar
  27. 27.
    Zhang H, Sun M, Wang X, Song Z, Tang J, Sun J (2017) Smart jump: Automated navigation suggestion for videos in moocs. In: Proceedings of the 26th international conference on world wide web companion, pp 331–339. International world wide web conferences steering committeeGoogle Scholar
  28. 28.
    Zhou X, Chen L, Zhang Y, Qin D, Cao L, Huang G, Wang C (2017) Enhancing online video recommendation using social user interactions. The VLDB Journal, pp 1–20Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of InformaticsIonian UniversityCorfuGreece

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