[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,2017,12(4):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,2017,12(4):498-503.[doi:10.11992/tis.201607023]
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ZHANG Donghui,CHENG Xianyi.Research on computation of affect in public opinion sentences from the cognition viewpoint[J].CAAI Transactions on Intelligent Systems,2017,12(4):498-503.[doi:10.11992/tis.201607023]
认知视角下的舆论观点句情感计算
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
12
期数:
2017年第4期
页码:
498-503
栏目:
学术论文—自然语言处理与理解
出版日期:
2017-08-25
- Title:
- Research on computation of affect in public opinion sentences from the cognition viewpoint
- Keywords:
- cognitive; sentiment computer; public opinion sentence; energy of view; active; negative; semantic; coarse-grained; fine granularity
- 分类号:
- TP391.1
- 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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- 相似文献/References:
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[1]陈建美,林鸿飞,杨志豪.基于语法的情感词汇自动获取[J].智能系统学报,2009,4(2):100.
CHEN Jian-mei,LIN Hong-fei,YANG Zhi-hao.Automatic acquisition of emotional vocabulary based on syntax[J].CAAI Transactions on Intelligent Systems,2009,4():100.
[2]夏 凡,王 宏.基于局部异常行为检测的欺骗识别研究[J].智能系统学报,2007,2(5):12.
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[3]王 巍,王志良,郑思仪,等.人机交互中的个性化情感模型[J].智能系统学报,2010,5(1):10.
WANG Wei,WANG Zhi-liang,ZHENG Si-yi,et al.Affective model in humanrobot interaction[J].CAAI Transactions on Intelligent Systems,2010,5():10.
[4]王? 巍,王志良,谷学静,等.隐马尔可夫情感模型的复合情绪生成[J].智能系统学报,2010,5(6):545.
WANG Wei,WANG Zhi-liang,GU Xue-jing,et al.Complex emotion generating of an emotion model based on HMM[J].CAAI Transactions on Intelligent Systems,2010,5():545.
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