Opinion Mining for Educational Video Lectures

The search for relevant educational videos is a time consuming process for the users. Furthermore, the increasing demand for educational videos intensifies the problem and calls for the users to utilize whichever information is offered by the hosting web pages, and choose the most appropriate one. This research focuses on the classification of user views, based on the comments on educational videos, into positive or negative ones. The aim is to give users a picture of the positive and negative comments that have been recorded, so as to provide a qualitative view of the final selection at their disposal. The present paper's innovation is the automatic identification of the most important words of the verbal content of the video lectures and the filtering of the comments based on them, thus limiting the comments to the ones that have a substantial semantic connection with the video content.