Constructing context-aware sentiment lexicons with an asynchronous game with a purpose

Abstract

One of the reasons sentiment lexicons do not reach human-level performance is that they lack the contexts that define the polarities of words. While obtaining this knowledge through machine learning would require huge amounts of data, context is commonsense knowledge for people, so human computation is a better choice. We identify context using a game with a purpose that increases the workers' engagement in this complex task. With the contextual knowledge we obtain from only a small set of answers, we already halve the sentiment lexicons' performance gap relative to human performance. © 2014 Springer-Verlag Berlin Heidelberg.