一种情感判别分析体系在汽车品牌舆情管理中的应用

宋云生

doi:10.11959/j.issn.2096-0271.2017061

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一种情感判别分析体系在汽车品牌舆情管理中的应用

专题：工业大数据 | 更新时间：2024-06-03

- 一种情感判别分析体系在汽车品牌舆情管理中的应用
- Application of an emotion discriminant analysis system in the management of automobile brand
- 大数据 2017年3卷第6期页码：2017061
- 作者机构：
- 作者简介：
  
  [ "宋云生（1985-），男，深圳联友科技有限公司数据挖掘工程师，主要研究方向为自然语言理解及深度学习。" ]
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2017061
  中图分类号： TP391
- 网络首发：2017-11，
  
  纸质出版：2017-11-20
- 稿件说明：
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宋云生. 一种情感判别分析体系在汽车品牌舆情管理中的应用[J]. 大数据, 2017,3(6):2017061.

Yunsheng SONG. Application of an emotion discriminant analysis system in the management of automobile brand[J]. Big Data Research, 2017, 3(6): 2017061.
宋云生. 一种情感判别分析体系在汽车品牌舆情管理中的应用[J]. 大数据, 2017,3(6):2017061. DOI： 10.11959/j.issn.2096-0271.2017061.

Yunsheng SONG. Application of an emotion discriminant analysis system in the management of automobile brand[J]. Big Data Research, 2017, 3(6): 2017061. DOI： 10.11959/j.issn.2096-0271.2017061.

摘要

品牌舆情管理涉及文本、语音等自然语言产物的处理，如挖掘文本内涵的情感、观点等并对其量化，才能进一步分析品牌所处的舆论环境。对自然语言中情感的量化即情感判别分析，针对传统的基于词典的情感分析和基于监督模型的情感分析存在的不足，提出了一种新的情感分析系统，并结合朴素贝叶斯分类算法，提高了情感分析的准确率，并增强了量化分析情感强度的能力。经测试，提出的文本情感分析引擎的情感判别准确率高于常见的分析方法，且不具有非常明显的行业特异性。

Abstract

Brand public opinion management involves text

voice and other natural language processing

such as mining the emotions and views of the text and quantifies it.The quantification of emotion in natural language is the emotion discriminant analysis.Considering the disadvantage in the traditional sentiment analysis that based on emotional dictionary and supervision model based sentiment analysis system

a new sentiment analysis system was proposed

and combined with the Naive Bayesian classification algorithm

the accuracy of sentiment analysis was improved

and the ability of quantitative analysis of emotional strength was enhanced.The sentiment discrimination accuracy of the proposed text sentiment analysis engine is higher than that of the common analysis method

and there is no decent of accuracy in out-ofsample texts from different industries.

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references

ZHAO Y Y , QIN B , LIU T . Sentiment analysis [J ] . Journal of Software , 2010 , 21 ( 8 ): 1834 - 1848 .

KHARDE V A , SONAWANE S . Sentiment analysis of twitter data:a survey of techniques [J ] . Computer Science , 2016 : arXiv : 1601 .06971.

KU L W , LO Y S , CHEN H H . Using polarity scores of words for sentencelevel opinion extraction [C ] // The 6th NTCIR-6 Workshop Meeting , May 15 - 18 , 2007 , Toyko,Japan .[S.l.:s.n. ] , 2007 .

KAJI N , KITSUREGAWA M . Building lexicon for sentiment analysis from massive collection of HTML documents [C ] // The 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning , June 28 - 30 , 2007 , Prague,Czech Republic .[S.l.:s.n. ] , 2007 .

PANG B , LEE L . Opinion mining and sentiment analysis [J ] . IEEE Security and Privacy , 2008 , 2 ( 1 ): 459 - 526 .

JUNGHERR A . Twitter use in election campaigns:a systematic literature review [J ] . Journal of Information Technology ＆ Politics , 2016 , 13 ( 1 ): 72 - 91 .

宋玮，訾红 . “被就业”事件网络舆情分析 [J ] . 河北广播电视大学学报， 2010 , 15 ( 3 ): 106 - 108 .

SONG W , ZI H . Analysis of online public opinion on the “be job hunted”scandal [J ] . Journal of Hebei Radio ＆ TV University , 2010 , 15 ( 3 ): 106 - 108 .

赵晋 . 浅析网络舆情分析在企业品牌危机管理中的应用 [J ] . 新闻世界， 2008 ( 12 ): 97 - 98 .

ZHAO J . Analysis of the application of network public opinion in enterprise brand crisis management [J ] . News World , 2008 ( 12 ): 97 - 98 .

AKKAYA C , CONRAD A , WIEBE J , et al . Amazon mechanical Turk for subjectivity word sense disambiguation [C ] // The NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk , June 6 , 2010 , Los Angeles,California . Stroudsburg : Association for Computational Lin guistics , 2010 .

TABOADA M , BROOKE J , TOFILOSKI M , et al . Lexicon-based methods for sentiment analysis [J ] . Computational Linguistics , 2011 , 37 ( 2 ): 267 - 307 .

张成功，刘培玉，朱振方，等 . 一种基于极性词典的情感分析方法 [J ] . 山东大学学报 ( 理学版 )， 2012 , 47 ( 3 ): 50 - 53 .

ZHANG C G , LIU P Y , ZHU Z F , et al . A sentiment analysis method based on a polarity lexicon [J ] . Journal of Shandong University , 2012 , 47 ( 3 ): 50 - 53 .

FIRMINO A A , PAIVA A C D . A comparison of SVM versus naiveBayes techniques for sentiment analysis in tweets:a case study with the 2013 FIFA confederations cup [C ] // The 20th Brazilian Symposium on Multimedia and the Web , November 18 - 21 , 2014 , João Pessoa,Brazil . New York : ACM Press , 2014 : 123 - 130 .

孙建旺，吕学强，张雷瀚 . 基于词典与机器学习的中文微博情感分析研究 [J ] . 计算机应用与软件， 2014 , 31 ( 7 ): 177 - 181 .

SUN J W , LV X Q , ZHANG L H . On sentiment analysis of Chinese microblogging based on lexicon and machine learning [J ] . Computer Applications and Software , 2014 , 31 ( 7 ): 177 - 181 .

关雅夫 . 基于主动学习的微博情感分析方法研究 [D ] . 长春：吉林大学， 2017 .

GUAN Y F . Research on microblog sentiment analysis based on active learning [D ] . Changchun : Jilin University , 2017 .

樊娜，安毅生，李慧贤 . 基于K-近邻算法的文本情感分析方法研究 [J ] . 计算机工程与设计， 2012 , 33 ( 3 ): 1160 - 1164 .

FAN N , AN Y S , LI H X . Research on analyzing sentiment of texts based on k-nearest neighbor algorithm [J ] . Computer Engineering and Design , 2012 , 33 ( 3 ): 1160 - 1164 .

李英 . 基于词性选择的文本预处理方法研究 [J ] . 情报科学， 2009 , 27 ( 5 ): 717 - 719 .

LI Y . Research on the text pretreatment based on part of speech selection [J ] . Information Science , 2009 , 27 ( 5 ): 717 - 719 .

张宁 . 基于语义的中文文本预处理研究 [D ] . 西安：西安电子科技大学， 2011 .

ZHANG N . Research of chinese text preprocessing based on semantic [D ] . Xi'an : Xidian University , 2011 .

TOKUHISA R , INUI K , MATSUMOTO Y . Emotion classification using massive examples extracted from the Web [C ] // The 22nd International Conference on Computational Linguistics , August 18 - 22 , 2008 , Manchester,United Kingdom . Stroudsburg : Association for Computational Linguistics , 2008 : 881 - 888 .

YANG Y H , LIU C C , CHEN H H . Music emotion classification:a fuzzy approach [C ] // The 14th ACM international conference on Multimedia , October 23 - 27 , 2006 , Santa Barbara,USA . New York : ACM Press , 2006 : 81 - 84 .

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