An Unsupervised Neural Model for Aspect Based Opinion Mining

The paper approaches aspect based opinion mining, which uses an unsupervised neural network as the opinion classifier. The neural network is an extension of the Growing Hierarchical Self-organizing Maps (GHSOM). In the aspect based sentiment analysis, we exploit the fact that the binary relations of syntactic dependency, extracted from product reviews, very often express relations between an aspect of the reviewed product and an opinion towards that aspect. We use the Growing Hierarchical Self-organizing Maps to classify pairs built according to the dependency relations, more exactly to classify pairs of the form (aspect, opinion bearing word). With this classification, we discover whether the various text mentions of the aspects of the target entity (such as the aspects of a product) are opinionated with positive or negative sentiment in the text of a review. We classify these pairs against a domain specific taxonomy of aspects, which also includes (positive/ negative) opinions associated with the aspects. Since it is based on classification against an ontology, our approach is semantic oriented. We tested our system on a collection of reviews about photo cameras.