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山东大学学报（理学版） ›› 2017, Vol. 52 ›› Issue (7): 80-90.doi: 10.6040/j.issn.1671-9352.5.2016.034

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微博短文本的情绪分析方法

施寒潇,厉小军,郝腾达,柳虹,朱柳青   

  1. 浙江工商大学计算机与信息工程学院, 浙江 杭州 310018
  • 收稿日期:2016-10-11 出版日期:2017-07-20 发布日期:2017-07-07
  • 作者简介:施寒潇(1977— ),男,博士,副教授,研究方向为社会网络、情绪分析. E-mail:hxshi@mail.zjgsu.edu.cn
  • 基金资助:
    国家社会科学基金资助项目(14BTQ047)

Emotion analysis on Microblog short text

SHI Han-xiao, LI Xiao-jun, HAO Teng-da, LIU Hong, ZHU Liu-qing   

  1. School of Computer Science &
    Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, Zhejiang, China
  • Received:2016-10-11 Online:2017-07-20 Published:2017-07-07

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摘要： 面向微博短文本的情绪分析研究是当前的研究热点。提出了利用依存句法对微博短文本进行分析,抽取关系对,并设计相应的方法用于情感计算,其结果作为特征加入到情绪句判别模型之中;同时设计出情绪句判别规则,在分类模型之前或者之后利用规则进行预处理或者后处理,提高情绪句的判别正确率;最后使用NLP&2013中文微博数据,通过实验证明研究方法的有效性,在性能指标上相比评测最好成绩有了进一步提高。

关键词: 情绪分析, 依存句法, 情绪短文本, 特征抽取

Abstract: Studies on emotion analysis oriented to Microblog short text are a hot issue in present research. In this paper, dependency grammar was used to analyse Microblog short text and extract relation pair. We proposed the corresponding methods to compute sentiment value and add the corresponding results to discriminant model of sentiment sentences as features. We also proposed discrimination rules of sentiment sentences and utilized the rules to correspondingly preprocess or postprocess before or after the classification model in order to improve the discrimination rate of sentiment sentences. Finally, we used NLP&CC’2013 Chinese Microblog data to testify the effectiveness of our method through experiments. Results show our works have better performance comparing to the best evaluation in that year.

Key words: emotion analysis, feature extraction, dependency syntax, emotion short text

中图分类号: 

  • TP391
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