Sentiment Analysis: A tool for Rating Attribution to Content in Recommender Systems

Collaborative filtering techniques are commonly used in social networking environ- ments for proposing user connections or interesting shared resources. While metrics based on ac- cess patterns and user behavior 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 propose the introduction of sentiment analysis techniques on user comments regarding an edu- cational resource in order to extract the opinion of a user for the quality of the latter and take into account its quality as perceived by the community before proposing the resource to another user.