A Recurrent Neural Network for Sentiment Quantification

Simply put, the reason is that classifiers are Quantification can in principle be solved by classifying all the unlabelled typically trained to minimize classification error, which is by and items and counting how many of them have been attributed large proportional to (FP FN), while a good quantifier should to each class. However, this "classify and count" approach has been be trained to minimise quantification error, which is by and large shown to yield suboptimal quantification accuracy; this has established proportional to FP FN (where TP, FP, FN, T N denote the usual quantification as a task of its own, and given rise to a number counts from a binary contingency table). of methods specifically devised for it. We propose a recurrent neural In this paper we tackle quantification in a binary setting, and propose network architecture for quantification (that we call QuaNet) that a recurrent neural network architecture (that we call QuaNet) observes the classification predictions to learn higher-order "quantification that observes the classification predictions to learn higher-order embeddings", which are then refined by incorporating "quantification embeddings", which are then refined by incorporating quantification predictions of simple classify-and-count-like methods.