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Web Crawler for Indexing Video e-Learning Resources: A YouTube Case Study

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  • Bogdan IANCU

Abstract

The main objective of the current paper is to develop and validate an algorithm focused on au-tomatically indexing YouTube e-learning resources about a certain domain of interest. After identifying the keywords specific to the desired domain, a web crawler is developed for evaluat-ing video resources (from the YouTube platform) in terms of relevance for that domain. Once the most relevant video resources are found, they are indexed with the usage of a NER engine applied on their transcripts. In this manner, semantic queries can be used further in order to find exactly the needed information inside these multimedia resources. The crawler will repeat the indexing process daily in order to maintain the repository of semantically indexed videos up to date. The final chapter presents the obtained results together with the validation of the model.

Suggested Citation

  • Bogdan IANCU, 2019. "Web Crawler for Indexing Video e-Learning Resources: A YouTube Case Study," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 23(2), pages 15-24.
    • Handle: RePEc:aes:infoec:v:23:y:2019:i:2:p:15-24
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    References listed on IDEAS

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    1. Bogdan IANCU, 2019. "Evaluating Google Speech-to-Text API's Performance for Romanian e-Learning Resources," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 23(1), pages 17-25.
    2. Bogdan IANCU & Tiberiu Marian GEORGESCU, 2018. "Saving Large Semantic Data in Cloud: A Survey of the Main DBaaS Solutions," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 22(1), pages 5-16.
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