属性级情感分类任务旨在判断句子针对给定属性的情感极性, 因其广泛应用而备受关注. 该任务的关键在于识别给定属性相关的上下文描述, 并根据上下文内容判断发文者针对相应属性的情感倾向. 统计发现, 大约30%的评论中并不包含关于给定属性的明确情感描述, 但仍然传达了清晰的情感倾向, 这被称为隐式情感表达. 近年来, 基于注意力机制的神经网络方法在情感分析中得到了成功应用. 但该类方法只能捕捉属性相关的显式情感描述, 而缺乏对隐含情感的有效分析和挖掘, 且往往将属性词与句子上下文分别建模, 使得属性词的表示缺乏上下文语义. 针对以上两个问题, 提出一种交叉融合属性局部和句子全局上下文信息的属性级情感分类方法, 并根据隐式和显式情感表达句子不同的分类难度采用课程学习提高模型的分类性能. 实验表明, 所提方法不仅对显式情感表达句子的属性情感倾向识别准确率高, 而且能够有效学习隐式情感表达句子的情感类别.
Aspect-level sentiment classification task, which aims to determine the sentiment polarity of a given aspect, has attracted increasing attention due to its broad applications. The key to this task is to identify contextual descriptions relevant to the given aspect and predict the aspect-related sentiment orientation of the author according to the context. Statistically, it is found that close to 30% of reviews convey a clear sentiment orientation without any explicit sentiment description of the given aspect, which is called implicit sentiment expression. Recent attention mechanism-based neural network methods have gained great achievement in sentiment analysis. However, this kind of method can only capture explicit aspect-related sentiment descriptions but fails to effectively explore and analyze implicit sentiment, and it often models aspect words and sentence contexts separately, which makes the expression of aspect words lack contextual semantics. To solve the above two problems, this study proposes an aspect-level sentiment classification method that integrates local aspect information and global sentence context information and improves the classification performance of the model by curriculum learning according to different classification difficulties of implicit and explicit sentiment sentences. Experimental results show that the proposed method not only has a high accuracy in identifying the aspect-related sentiment orientation of explicit sentiment sentences but also can effectively learn the sentiment categories of implicit sentiment sentences.