Latent Dynamic Model with Category Transition Constraint for Opinion Classification

Latent models for opinion classification are studied. Training a probabilistic model with a number of latent variables is found unstable in some cases; thus this paper presents how to construct a stable model for opinion classification by constraining classification transitions. The baseline model is a CRF classification model with plural latent variables, dynamically constructed from the dependency parsed tree. The aim of the baseline model is to have each latent variable convey a partial sentiment of the input sentence which is not explicitly given in the training data, and the complete sentiment of the sentence is computed by summing up such partial sentiment where those latent variables hold. Since such a conventional model has many degeneracies in principle, a model with a category transition constraint is proposed, which is expressed by a novel penalty term in the objective function for training the model. The constraint is such that the sentiment of a partial sentence more likely propagates to the same sentiment of the complete sentence, rather than to another sentiment. The effectiveness and the robustness of the proposed model are confirmed by the experiments on binary as well as multi-class opinion classification task.