面向问答文本的属性级情感分类研究

计算机科学 ›› 2019, Vol. 46 ›› Issue (11A): 5-8.

面向问答文本的属性级情感分类研究

江明奇¹, 李逸薇², 刘欢¹, 李寿山¹

(苏州大学计算机科学与技术学院江苏苏州215006)¹;
(香港理工大学中文及双语学系香港999077)²

出版日期:2019-11-10 发布日期:2019-11-20
通讯作者: 李寿山(1980-),男,教授,主要研究方向为自然语言处理、情感分析,E-mail:lishoushan.suda.edu。
作者简介:江明奇(1994-),男,硕士生,主要研究方向为自然语言处理、情感分析,E-mail:mqjiang@stu.suda.edu.cn。
基金资助:
本文受国家自然科学基金(61672366)资助。

Attribute Sentiment Classification Towards Question-answering Text

JIANG Ming-qi¹, LEE Sophia Yat Mei², LIU Huan¹, LI Shou-shan¹

(School of Computer Science & Technology,Soochow University,Suzhou 215006,China)¹;
(Department of Chinese and Bilingual Studies,Hong Kong Polytechnic University,Hong Kong 999077,China)²

Online:2019-11-10 Published:2019-11-20

摘要/Abstract

摘要： 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。

关键词: 情感分析, 问答文本, 注意力机制

Abstract: The goal of conditional sentiment analysis is getting the sentiment polarity of whole text,which is a coarsetask.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 attribute on question-answering 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.

Key words: Attention mechanism, Question-answering text, Sentiment analysis

中图分类号:

TP391

江明奇, 李逸薇, 刘欢, 李寿山. 面向问答文本的属性级情感分类研究[J]. 计算机科学, 2019, 46(11A): 5-8. https://doi.org/

JIANG Ming-qi, LEE Sophia Yat Mei, LIU Huan, LI Shou-shan. Attribute Sentiment Classification Towards Question-answering Text[J]. Computer Science, 2019, 46(11A): 5-8. https://doi.org/

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