Aspect-object alignment using integer linear programming

Target extraction is an important task in opinion mining, in which a complete target consists of an aspect and its corresponding object. However, previous work always simply considers the aspect as the target and ignores an important element “object.” Thus the incomplete target is of limited use for practical applications. This paper proposes a novel and important sentiment analysis task: aspect-object alignment, which aims to obtain the correct corresponding object for each aspect, to solve the “object ignoring” problem. We design a two-step framework for this task. We first provide an aspect-object alignment classifier that incorporates three sets of features. However, the objects assigned to aspects in a sentence often contradict each other. To solve this problem, we impose two kinds of constraints: intra-sentence constraints and intersentence constraints, which are encoded as linear formulations and use Integer Linear Programming (ILP) as an inference procedure to obtain a final global decision in the second step. The experiments on the corpora of camera domain show the effectiveness of the framework.