一种情感判别分析体系在汽车品牌舆情管理中的应用

doi:10.11959/j.issn.2096-0271.2017061

摘要/Abstract

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。

关键词: 情感分析, 监督模型, 朴素贝叶斯, 自然语言处理

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.

Key words: sentiment analysis, supervised model, naive Bayes, natural language processing

中图分类号:

TP391

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): 55-64.

Yunsheng SONG. Application of an emotion discriminant analysis system in the management of automobile brand[J]. Big Data Research, 2017, 3(6): 55-64.

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情感词	类型
哀艳	1
安顿	1
爱不忍释	1
赞助	1
责任	1
阿Q	1
暗弱	-1
昂贵	-1
浅陋	-1

情感词	在正向文本中的DF（pDF）	在负向文本中的DF（nDF）
哀艳	0.000 255 102	8.430 28E-5
安顿	0.000 340 136	0.000 590 12
爱不忍释	0.000 255 102	8.430 28E-5
赞助	8.503 4E-5	8.430 28E-5
责任	0.001 530 612	0.003 877 93
阿Q	0.000 255 102	8.430 28E-5
暗弱	8.503 4E-5	0.000 337 211
昂贵	0.000 680 272	0.000 421 514
浅陋	8.503 4E-5	0.000 337 211

程度副词	权重
略微	0.8
稍微	0.8
非常	2
极端	2
至极	2

文本	情感词	在正向文本中的DF（pDF）	在负向文本中的DF（nDF）
我非常不喜欢喝茶	喜欢	0.02	0.1

情感词	正向情感得分	负向情感得分
喜欢	0.051 161 3	0.009 133 5
保养贵	0.000 020 8	0.001 808 3
乱七八糟	0.000 541 6	0.001 164 7
发疯	0.000 041 7	0.000 122 6
创新	0.000 312 5	0.000 245 2
渗油	0.000 020 8	0.001 409 9
漏油	0.000 020 8	0.002 114 8
…	…	…