Earticle

초록

영어

Discovery and subsequent effective retrieval of useful user generated content depends on proper meta-data annotation implemented on an object such as a title and Keywords. In this study, a simpler unsupervised non graph-based algorithm for extracting keywords is proposed. A novel key phrases chunking approach was adopted; this utilizes words sequences as they appear in the original document. The simple but effective Term frequency-inverse document frequency (tf-idf) weighting scheme was exploited to rank the novelty created key-phrases. Comparing to a similar algorithm that uses three metrics weighting scheme, the tf-idf yielded a precision of 89%.Thus, the application of tf-idf algorithm on YouTube’s metadata based keywords shows to be useful approach in its objectivity.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Machine Learning Approaches
  2.2. Key Techniques
  2.3. TF-IDF for Single Document
 3. The Algorithm Description
 4. Experimental Result
 5. Extension to YouTube Videos
 6. Discussion
 7. 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; Dar es salaam Institute of Technology, Tanzania. ]
• Gaudence Uwamahoro [ Central South University, Changsha, Hunan, China; Dar es salaam Institute of Technology, Tanzania. ]

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