Analyzing semantic orientation of terms using Affinity Propagation

Abstract

The aim of term semantic orientation analysis is to mine the sentiment polarity of words and phrases from their contexts. This paper presents a novel algorithm called Affinity Propagation to analyze semantic orientations of terms. Specifically, we build an informative graph from text corpus using an efficient Word Activation Force model and regard each term as a node in the graph. Then we propagate opinionated information over the whole graph using only a small number of seed terms. We finally utilize affinity vectors rather than context vectors to detect term polarities and construct the polarity lexicons. Evaluations on our proposed algorithm show its advantages over the state-of-the-art algorithms. And further improvements can be obtained by combining Affinity Propagation with Pointwise Mutual Information.