[1]张冬慧,程显毅.认知视角下的舆论观点句情感计算[J].智能系统学报,2017,(04):498-503.[doi:10.11992/tis.201607023]
 ZHANG Donghui,CHENG Xianyi.Research on computation of affect in public opinion sentences from the cognition viewpoint[J].CAAI Transactions on Intelligent Systems,2017,(04):498-503.[doi:10.11992/tis.201607023]
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认知视角下的舆论观点句情感计算(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
期数:
2017年04期
页码:
498-503
栏目:
出版日期:
2017-08-25

文章信息/Info

Title:
Research on computation of affect in public opinion sentences from the cognition viewpoint
作者:
张冬慧1 程显毅2
1. 北京信息科技大学 计算中心, 北京 100192;
2. 南通大学 计算机科学与技术学院, 江苏 南通 226019
Author(s):
ZHANG Donghui1 CHENG Xianyi2
1. Computing Center, Beijing Information Science & Technology University, Beijing 100192, China;
2. School of Computer Science and Technology, Nantong University, Nantong 226019, China
关键词:
认知情感计算舆论观点句观点的能量消极积极语义粗粒度细粒度
Keywords:
cognitivesentiment computerpublic opinion sentenceenergy of viewactivenegativesemanticcoarse-grainedfine granularity
分类号:
TP391.1
DOI:
10.11992/tis.201607023
摘要:
针对目前观点分析方法局限于传统的文本分析技术,只能将舆论观点句分为肯定和否定两极或确定每一极的程度(粗粒度),不能进一步给出舆论观点句是积极的还是消极的程度的问题。本文从认知学角度研究细粒度语义情感计算框架。提出了一种舆情观点句的定量分析方法,该方法将对于某话题的文本集合作为输入,输出一个实数表示文本中所表达观点的能量。本文在NLPIR共享平台上进行了相关实验,给出了粗粒度情感和细粒度情感对观点句识别的对比实验,实验表明,两种方法对观点句的识别性能相差不大;对非观点句细粒度方法好于粗粒度方法。
Abstract:
The current viewpoint analysis method is limited to the traditional text analysis technology, whereby a public opinion sentence can only be divided into positive and negative poles and the extent of each pole (coarse-grained) determined. It is difficult to determine whether a public opinion sentence is active or passive. In this paper, we discuss a computation framework for fine-grained semantic sentiments from the cognitive science viewpoint and propose a quantitative analysis method for public opinion sentences. This method takes the text collection of some topic as input and uses a real number to represent the energy of a viewpoint in the text. We conducted an experiment using the Natural Language Processing and Information Retrieval (NLPIR) sharing platform and a contrasting experiment with respect to view recognition by comparing coarse-grained and fine-grained affects. The experimental results show that the two methods have the same recognition performance regarding sentence viewpoints. For no-opinion sentences, the fine-grained method performs better than the coarse-grained method.

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备注/Memo

备注/Memo:
收稿日期:2016-07-23。
基金项目:国家自然科学基金项目(61340037).
作者简介:张冬慧,女,1969年生,博士,主要研究方向为自然语言处理、计算机网络教育应用、知识工程。参与出版教材2部,发表学术论文5篇;程显毅,男,1956年生,教授,博士,主要研究方向为知识工程、大数据应用、自然语言处理。主持国家自然科学基金2项、江苏省重点科技攻关项目1项、省部级项目6项。获省优秀教学成果一等奖1项,二等奖1项。出版专著5部,教材3部,发表学术论文100余篇。
通讯作者:程显毅,E-mail:xycheng@ntu.edu.cn.
更新日期/Last Update: 2017-08-25