Comparative study on subject classification of academic videos using noisy transcripts

With the advance of Web technologies, the number of "academic" videos available on the Web (e.g., online lectures, web seminars, conference presentations, or tutorial videos) has increased explosively. A fundamental task of managing such videos is to classify them into relevant subjects. For this task, most of current content providers rely on keywords to perform the classification, while active techniques for automatic video classification focus on utilizing multi-modal features. However, in our settings, we argue that both approaches are not sufficient to solve the problem effectively. Keywords based method is very limited in terms of accuracy, while features based one lacks semantics to represent academic subjects. Toward this problem, in this paper, we propose to transform the video subject classification problem into the text categorization problem by exploiting the extracted transcripts of videos. Using both real and synthesized data, (1) we extensively study the validity of the proposed idea, (2) we analyze the performance of different text categorization methods, and (3) we study the impact of various factors of transcripts such as quality and length towards academic video classification problem.