Stance detection via sentiment information and neural network model

Stance detection aims to automatically determine whether the author is in favor of or against a given target.In principle,the sentiment information of a post highly influences the stance.In this study,we aim to leverage the sentiment information of a post to improve the performance of stance detection.However,conventional discrete models with sentimental features can cause error propagation.We thus propose a joint neural network model to predict the stance and sentiment of a post simultaneously,because the neural network model can learn both representation and interaction between the stance and sentiment collectively.Specifically, we first learn a deep shared representation between stance and sentiment information,and then use a neural stacking model to leverage sentimental information for the stance detection task.Empirical studies demonstrate the effectiveness of our proposed joint neural model.