Earticle

초록

영어

Given the importance of the textual information in content retrieval, it is desirable that the textual representation of educational videos contents in social media platforms like YouTube capture the semantics of what is really in content they represent. Such coherent textual representations are important in objective video content retrieval, repurposing, reuse and sense- making of the content. In this study,the Automatic Speech Recognition (ASR) in the video tracks was leveraged to supplement the insufficient video content representations done through video title alone. The Latent Dirichlet allocation (LDA) implementation of Gibb’s sampling topic modeling approach was used to evaluate the suitability of various textual representations for YouTube educational videos and extract the candidate topic that extends well the original YouTube keywords. The results show that in topics space, YouTube ASR script performs well as a representative textual source in dominant topic than the combined textual representations. The automatic keywords extension obtained using our method add value to applications that use tags for content discovery or retrieval

목차

Abstract
 1. Introduction
 2. Related Work
 3. Method Description
 4. Experimental Result
  4.1 Textual Representations
  4.2 The Algorithm for Extending the YouTube Keywords
 5. Conclusion
 References

키워드

저자

• Ambele Robert Mtafya [ Central South University, Changsha, Hunan, China; Dar es salaam Institute of Technology, Tanzania ]
• Dongjun Huang [ Central South University, Changsha, Hunan, China ]
• Gaudence Uwamahoro [ Central South University, Changsha, Hunan, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605