Please wait a minute...
Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (8): 85-91    DOI: 10.11925/infotech.2096-3467.2017.08.10
Orginal Article Current Issue | Archive | Adv Search |
Analyzing Textual Sentiment Based on HNC Theory
Gao Ge, Luo Junmei, Wang Yu()
Faculty of Management and Economics, Dalian University of Technology, Dalian 116024, China
Download: PDF (727 KB)   HTML ( 1
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This sutdy proposes a new method to conduct sentiment analysis with comment texts, aiming to deal with the issues facing new online terms. [Methods] Based on the Hierarchical Network of Concepts (HNC) theory, we defined symbols for the new words, which could be processed more efficiently. [Results] The proposed method analyzed the sentiment of the textual message effectively. [Limitations] Our method could only process short texts, while we still need to manually create symbols for the new words. [Conclusions] We proposed an effective way to conduct sentiment analysis.

Key wordsComment Text      Sentiment Analysis      Hierarchical Network of Concepts (HNC)     
Received: 27 May 2017      Published: 28 September 2017
ZTFLH:  TP391  

Cite this article:

Gao Ge,Luo Junmei,Wang Yu. Analyzing Textual Sentiment Based on HNC Theory. Data Analysis and Knowledge Discovery, 2017, 1(8): 85-91.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.08.10     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I8/85

概念节点 HNC符号 权值
极、最 j60d01、j60c44 1.5
j60c43 1.3
稍微 j60c41 1.1
适度 j60c42、j60e41 1
不够 j60e42 0.8
过分 j60e43 -0.5
否定 ! -1
情感词 基础
情感值
情感词 基础
情感值
情感词 基础
情感值
0 精细 1 热情 1
1 厉害 1 神速 1
不错 1 流畅 1 完美 1
0 -1 迅速 1
方便 1 耐心 1 正常 1
1 耐用 1 及时 1
合适 1 清晰 1 简陋 -1
划算 1 结实 1
程度副词 有些 相当 特别 非常 比较
变动权值 1.1 1.3 1.3 1.3 1.5 1.3 1 1.3 1
[1] 薛丽敏, 李殿伟, 肖斌. 中文文本情感倾向性五元模型研究[J]. 通信技术, 2011, 44(7): 130-132.
doi: 10.3969/j.issn.1002-0802.2011.07.047
[1] (Xue Limin, Li Dianwei, Xiao Bin.Study on Novel Quintuple Model for Chinese Text Sentiment Orientation[J]. Communications Technology, 2011, 44(7): 130-132.)
doi: 10.3969/j.issn.1002-0802.2011.07.047
[2] 朱嫣岚, 闵锦, 周雅倩, 等. 基于HowNet的词汇语义倾向计算[J]. 中文信息学报, 2006, 20(1): 14-20.
doi: 10.3969/j.issn.1003-0077.2006.01.003
[2] (Zhu Yanlan, Min Jin, Zhou Yaqian, et al.Semantic Orientation Computing Based on HowNet[J]. Journal of Chinese Information Processing, 2006, 20(1): 14-20.)
doi: 10.3969/j.issn.1003-0077.2006.01.003
[3] 聂卉, 容哲. 面向评论效用评估的文本情感特征提取[J]. 现代图书情报技术, 2015(7-8): 113-121.
[3] (Nie Hui, Rong Zhe.Review Helpfulness Prediction Research Based on Review Sentiment Feature Sets[J]. New Technology of Library and Information Service, 2015(7-8): 113-121.)
[4] 兰秋军, 刘文星, 李卫康, 等. 融合句法信息的金融论坛文本情感计算研究[J]. 现代图书情报技术, 2016(4): 64-71.
[4] (Lan Qiujun, Liu Wenxing, Li Weikang, et al.Sentiment Analysis of Financial Forum Textual Message[J]. New Technology of Library and Information Service, 2016(4): 64-71.)
[5] 何跃, 肖敏, 张月. 结合话题相关性的热点话题情感倾向研究[J]. 数据分析与知识发现, 2017, 1(3): 46-53.
[5] (He Yue, Xiao Min, Zhang Yue.Sentiment Analysis of Trending Topics Based on Relevance[J]. Data Analysis and Knowledge Discovery, 2017, 1(3): 46-53.)
[6] 钟义信. 自然语言理解的全信息方法论[J]. 北京邮电大学学报, 2004, 27(4): 1-12.
doi: 10.3969/j.issn.1007-5321.2004.04.001
[6] (Zhong Yixin.Comprehensive Information Based Methodology for Natural Language Understanding[J]. Journal of Beijing University of Posts and Telecommunications, 2004, 27(4): 1-12.)
doi: 10.3969/j.issn.1007-5321.2004.04.001
[7] 樊康新. 基于多种情感特征的网络文本倾向性判别方法研究[J]. 电脑知识与技术, 2015, 11(22): 18-21.
[7] (Fan Kangxin.Research on the Method of Network Text Orientation Discrimination Based on Multiple Sentiment Features[J]. Computer Knowledge and Technology, 2015, 11(22): 18-21.)
[8] 刘玮楠. 基于HNC理论的网购评论情感倾向性分析研究[D]. 大连: 大连理工大学, 2013.
[8] (Liu Weinan.Research on Sentiment Orientation Analysis of Online-shopping Review Base-on HNC Theory [D]. Dalian: Dalian University of Technology, 2013.)
[9] 黄曾阳. HNC理论概要[J]. 中文信息学报, 1997, 11(4): 12-21.
[9] (Huang Zengyang.The Profile of HNC Theory[J]. Journal of Chinese Information Processing, 1997, 11(4): 12-21.)
[10] 唐兴全. HNC理论的五元组与词性[C]//自然语言理解与机器翻译——全国第六届计算语言学联合学术会议论文集. 2001.
[10] (Tang Xingquan.The Quintuple of HNC Theory and Part of Speech[C]//// Proceedings of the 6th Academic Conference on Computational Linguistics in China. 2001.)
[11] 李颖, 池毓焕. 对偶性概念的HNC阐释[J]. 中文信息学报, 2004, 18(3): 39-46.
doi: 10.3969/j.issn.1003-0077.2004.03.006
[11] (Li Ying, Chi Yuhuan.Re-Categorization of Antithesis Based on HNC Theory[J]. Journal of Chinese Information Processing, 2004, 18(3): 39-46.)
doi: 10.3969/j.issn.1003-0077.2004.03.006
[12] HNC(概念层次网络)理论[C]//中国中文信息学会第六次全国会员代表大会暨成立二十五周年学术会议中文信息处理重大成果汇报展资料汇编. 中国中文信息学会, 2006.
[12] (HNC (Hierarchical Network of Concepts) Theory[C]//// Proceedings of the 25th Anniversary Academic Conference of Chinese Information Society. 2006.)
[13] 王昌厚, 王菲. 使用基于模式的Bootstrapping方法抽取情感词[J]. 计算机工程与应用, 2014, 50(1): 127-129.
doi: 10.3778/j.issn.1002-8331.1203-0323
[13] (Wang Changhou, Wang Fei.Extracting Sentiment Words Using Pattern Based Bootstrapping Method[J]. Computer Engineering and Applications, 2014, 50(1): 127-129.)
doi: 10.3778/j.issn.1002-8331.1203-0323
[1] Xu Hongxia,Yu Qianqian,Qian Li. Studying Content Interaction Data with Topic Model and Sentiment Analysis[J]. 数据分析与知识发现, 2020, 4(7): 110-117.
[2] Jiang Lin,Zhang Qilin. Research on Academic Evaluation Based on Fine-Grain Citation Sentimental Quantification[J]. 数据分析与知识发现, 2020, 4(6): 129-138.
[3] Shi Lei,Wang Yi,Cheng Ying,Wei Ruibin. Review of Attention Mechanism in Natural Language Processing[J]. 数据分析与知识发现, 2020, 4(5): 1-14.
[4] Li Tiejun,Yan Duanwu,Yang Xiongfei. Recommending Microblogs Based on Emotion-Weighted Association Rules[J]. 数据分析与知识发现, 2020, 4(4): 27-33.
[5] Shen Zhuo,Li Yan. Mining User Reviews with PreLM-FT Fine-Grain Sentiment Analysis[J]. 数据分析与知识发现, 2020, 4(4): 63-71.
[6] Xue Fuliang,Liu Lifang. Fine-Grained Sentiment Analysis with CRF and ATAE-LSTM[J]. 数据分析与知识发现, 2020, 4(2/3): 207-213.
[7] Ying Tan,Jin Zhang,Lixin Xia. A Survey of Sentiment Analysis on Social Media[J]. 数据分析与知识发现, 2020, 4(1): 1-11.
[8] Hui Nie,Huan He. Identifying Implicit Features with Word Embedding[J]. 数据分析与知识发现, 2020, 4(1): 99-110.
[9] Yonghua Cen,Zhihao Tan,Chengyao Wu. Impacts of Financial Media Information on Stock Market: An Empirical Study of Sentiment Analysis[J]. 数据分析与知识发现, 2019, 3(9): 98-114.
[10] Weicong Lu,Jian Xu. Sentiment Analysis for Online User Reviews Based on Tripartite Network[J]. 数据分析与知识发现, 2019, 3(8): 10-20.
[11] Zhongxi You,Weina Hua,Xuelian Pan. Matching Book Reviews and Essential Sentiment Lexicons with Chinese Word Segmenters[J]. 数据分析与知识发现, 2019, 3(7): 23-33.
[12] Cuiqing Jiang,Yibo Guo,Yao Liu. Constructing a Domain Sentiment Lexicon Based on Chinese Social Media Text[J]. 数据分析与知识发现, 2019, 3(2): 98-107.
[13] Fen Chen,Xiaohuan Gao,Yue Peng,Yuan He,Chunxiang Xue. Identifying Weibo Opinion Leaders with Text Sentiment Analysis[J]. 数据分析与知识发现, 2019, 3(11): 120-128.
[14] Bengong Yu,Peihang Zhang,Qingtang Xu. Selecting Products Based on F-BiGRU Sentiment Analysis[J]. 数据分析与知识发现, 2018, 2(9): 22-30.
[15] Ziming Zeng,Qianwen Yang. Sentiment Analysis for Micro-blogs with LDA and AdaBoost[J]. 数据分析与知识发现, 2018, 2(8): 51-59.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn