计算机科学 ›› 2015, Vol. 42 ›› Issue (1): 210-214.doi: 10.11896/j.issn.1002-137X.2015.01.047

• 人工智能 • 上一篇    下一篇

情感不确定词句的分类方法比较研究

李飏,苗夺谦,张志飞   

  1. 同济大学计算机科学与技术系 上海201804 同济大学嵌入式系统与服务计算教育部重点实验室 上海201804,同济大学计算机科学与技术系 上海201804 同济大学嵌入式系统与服务计算教育部重点实验室 上海201804,同济大学计算机科学与技术系 上海201804 同济大学嵌入式系统与服务计算教育部重点实验室 上海201804
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金项目(61273304,61202170),高等学校博士学科点专项科研基金项目(20130072130004)资助

Sentiment Analysis of Words and Sentences with Uncertainty

LI Yang, MIAO Duo-qian and ZHANG Zhi-fei   

  • Online:2018-11-14 Published:2018-11-14

摘要: 语义不确定的词和句子在中文情感分析中有着重要意义。语义不确定的词一般是一个语义十分丰富的词,在表达中隐含着某种倾向性的评价;而语义不确定的句子一般表现为褒贬情感词相当,极性倾向不明显。以不确定词“好”为例对不确定句子进行特征设计,然后通过4种监督学习的分类方法对比实验说明支持向量机在处理不确定词和不确定句子的情感分析上有较好的效果。

关键词: 情感分析,不确定性,监督学习,支持向量机

Abstract: It is significant to analyze emotional uncertain words and sentences in Chinese text sentiment analysis.Emotional uncertain words are generally words with rich meaning,which implies some evaluation in the expression.Emotional uncertain sentences usually have the same size of positive words and negative words,so the emotion is not ob-vious.In this paper,using uncertain word “好” as an example,we designed features for the uncertain sentences.Then using four different classification algorithms of supervised learning to do experiments,we got the conclusion:SVM can better deal with the emotional uncertain words and sentences.

Key words: Sentimental analysis,Uncertainty,Supervised learning,SVM

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