Unsupervised Aspect Level Sentiment Analysis Using Self-Organizing Maps

Abstract

This paper presents an unsupervised method for aspect level sentiment analysis that uses the Growing Hierarchical Self-organizing Maps. Different sentences in a product review refer to different aspects of the reviewed product. We use the Growing Hierarchical Self-organizing Maps in order to classify the review sentences. This way we determine whether the various aspects of the target entity (e.g. a product) are opinionated with positive or negative sentiment in the review sentences. By classifying the sentences against a domain specific tree-like ontological taxonomy of aspects and sentiments associated with the aspects (positive/ negative sentiments), we really classify the opinion polarity as expressed in sentences about the different aspects of the target object. The approach proposed has been tested on a collection of product reviews, more exactly reviews about photo cameras.