面向问答文本的属性级情感分类研究

Abstract

The goal of conditional sentiment analysis is getting the sentiment polarity of whole text, which is a coarse task. Recently, with the improved technology, the sentiment analysis task is also refined, and the researchers hope to get sentiment polarity of given target of the text. This paper's purpose is getting the sentiment polarity of product at-tribute on question-answer text. To perform attribute sentiment classification towards QA text pair, this paper proposed a novel approach based on attention mechanism. Firstly, this paper concatenated the attribute information on answer words' vectors. Secondly, this paper leveraged LSTM models to encode the question text and answer text. Thirdly, this paper got the relation of question and answer by using attention mechanism and got the whole feature of answer. Finally, this paper got the result of whole feature by using classifier. Empirical studies demonstrate the effectiveness of the proposed approach to attribute sentiment classification towards question-answering text. 传统情感分析任务的目的是分析整个文本的情感极性，这是一种粗粒度的任务。近年来，随着技术的革新，情感分析任务也在不断细化，研究者们希望能获取关于文本中具体对象的情感极性。文中的研究任务是获取问答文本中关于产品属性的情感极性。针对问答文本的属性级情感分析问题，提出了一种基于注意力机制的方法。首先，将属性信息拼接到答案词向量上；其次，对答案文本和问题文本学习一个 ＬＳＴＭ 模型；然 后，通过注意力机制获得问题文本和答案文本的相关性，并根据相关性的重要程度获取答案文本的整体特征；最后，通过分类器输出最终的整体特征结果。实验结果表明，所提方法优于传统的属性级情感分析方法。