This paper proposes neural networks for integrating compositional and non-compositional sentiment in the process of sentiment composition, a type of semantic composition that optimizes a sentiment objective. We enable individual composition operations in a recursive process to possess the capability of choosing and merging information from these two types of sources. We propose our models in neural network frameworks with structures, in which the merging parameters can be learned in a principled way to optimize a well-defined objective. We conduct experiments on the Stanford Sentiment Treebank and show that the proposed models achieve better results over the model that lacks this ability.
['Xiaodan Zhu', 'Hongyu Guo', 'Parinaz Sobhani']
Neural Networks for Integrating Compositional and Non-compositional Sentiment in Sentiment Composition