Collaborative filtering recommendation of educational content in social environments utilizing sentiment analysis techniques

Abstract

Collaborative filtering techniques are commonly used in social networking environments for proposing user connections or interesting shared resources. While metrics based on access patterns and user behaviour produce interesting results, they do not take into account qualitative information, i.e. the actual opinion of a user that used the resource and whether or not he would propose it for use to other users. This is of particular importance on educational repositories, where the users present significant deviations in goals, needs, interests and expertise level. In this paper, we examine the benefits from introducing sentiment analysis techniques on user-generated comments in order to examine the correlation of an explicit rating with the polarity of an associated text, to retrieve additional explicit information from user comments when a standard rating is missing and expand tried recommendation calculation with qualitative information based on the community’s opinion before proposing the resource to another user.