Memory networks for fine-grained opinion mining

Abstract

Fine-grained opinion mining has attracted increasing attention recently because of its benefits for providing richer information compared with coarse-grained sentiment analysis. Under this problem, there are several existing works focusing on aspect (or opinion) terms extraction which utilize the syntactic relations among the words given by a dependency parser. These approaches, however, require additional information and highly depend on the quality of the parsing results. As a result, they may perform poorly on user-generated texts, such as product reviews, tweets, etc., whose syntactic structure is not precise. In this work, we offer an end-to-end deep learning model without any preprocessing. The model consists of a memory network that automatically learns the complicated interactions among aspect words and opinion words. Moreover, we extend the network with a multi-task manner to solve a finer-grained opinion mining problem, which is more challenging than the traditional fine-grained opinion mining problem. To be specific, the finer-grained problem involves identification of aspect and opinion terms within each sentence, as well as categorization of the identified terms at the same time. To this end, we develop an end-to-end multi-task memory network, where aspect/opinion terms extraction for a specific category is considered as a task, and all the tasks are learned jointly by exploring commonalities and relationships among them. We demonstrate state-of-the-art performance of our proposed model on several benchmark datasets.