论文标题
多标签的多标签学习网络，用于方面类别情感分析
Multi-Instance Multi-Label Learning Networks for Aspect-Category Sentiment Analysis
论文作者
论文摘要
方面类别情感分析（ACSA）旨在预测有关给定方面类别的句子的情感极性。为了检测句子中特定方面类别的情感，大多数以前的方法首先生成了方面类别的特定于方面类别的句子表示，然后基于表示形式预测情感极性。这些方法忽略了以下事实：句子中提到的方面类别的情绪是指示句子中的方面类别的单词情感的汇总，这导致了次优绩效。在本文中，我们提出了一个用于方面类别分析（AC-MIMLLN）的多标签学习网络（AC-MIMLLN），该网络将句子视为袋子，单词作为实例，以及将方面类别表示为方面类别的关键实例的单词。给定句子和句子中提到的方面类别，AC-Mimlln首先预测了实例的情感，然后找到了方面类别的关键实例，最后通过汇总关键实例情感来获得句子对方面类别的情感。三个公共数据集的实验结果证明了AC-Mimlln的有效性。
Aspect-category sentiment analysis (ACSA) aims to predict sentiment polarities of sentences with respect to given aspect categories. To detect the sentiment toward a particular aspect category in a sentence, most previous methods first generate an aspect category-specific sentence representation for the aspect category, then predict the sentiment polarity based on the representation. These methods ignore the fact that the sentiment of an aspect category mentioned in a sentence is an aggregation of the sentiments of the words indicating the aspect category in the sentence, which leads to suboptimal performance. In this paper, we propose a Multi-Instance Multi-Label Learning Network for Aspect-Category sentiment analysis (AC-MIMLLN), which treats sentences as bags, words as instances, and the words indicating an aspect category as the key instances of the aspect category. Given a sentence and the aspect categories mentioned in the sentence, AC-MIMLLN first predicts the sentiments of the instances, then finds the key instances for the aspect categories, finally obtains the sentiments of the sentence toward the aspect categories by aggregating the key instance sentiments. Experimental results on three public datasets demonstrate the effectiveness of AC-MIMLLN.
