• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mathematical Problems in Engineering >A Self-Adaptive Hidden Markov Model for Emotion Classification in Chinese Microblogs
【24h】

A Self-Adaptive Hidden Markov Model for Emotion Classification in Chinese Microblogs

机译：一种自适应隐马尔可夫模型在中国微博中的情感分类

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Microblogging is increasingly becoming one of the most popular online social media for people to express ideas and emotions. The amount of socially generated content from this medium is enormous. Text mining techniques have been intensively applied to discover the hidden knowledge and emotions from this huge dataset. In this paper, we propose a modified version of hidden Markov model (HMM) classifier, called self-adaptive HMM, whose parameters are optimized by Particle Swarm Optimization algorithms. Since manually labeling large-scale dataset is difficult, we also employ the entropy to decide whether a new unlabeled tweet shall be contained in the training dataset after being assigned an emotion using our HMM-based approach. In the experiment, we collected about 200,000 Chinese tweets from Sina Weibo. The results show that the F-score of our approach gets 76% on happiness and fear and 65% on anger, surprise, and sadness. In addition, the self-adaptive HMM classifier outperforms Naive Bayes and Support Vector Machine on recognition of happiness, anger, and sadness.
机译：微博正逐渐成为人们表达思想和情感的最受欢迎的在线社交媒体之一。从这种媒体社交产生的内容数量巨大。文本挖掘技术已被广泛应用于从这个庞大的数据集中发现隐藏的知识和情感。本文提出了一种隐马尔可夫模型（HMM）分类器的改进版本，称为自适应HMM，其参数通过粒子群优化算法进行了优化。由于手动标记大型数据集很困难，因此我们还使用熵来确定使用基于HMM的方法分配了情感后，训练数据集中是否应包含新的未标记推文。在实验中，我们从新浪微博收集了大约20万条中国推文。结果表明，我们的方法的F分数在幸福和恐惧方面获得76％，在愤怒，惊奇和悲伤方面获得65％。此外，自适应HMM分类器在识别幸福，愤怒和悲伤方面要优于朴素贝叶斯和支持向量机。

著录项

  • 来源
    《Mathematical Problems in Engineering》 |2015年第18期|987189.1-987189.8|共8页
  • 作者

    Liu Li; Luo Dashi; Liu Ming; Zhong Jun; Wei Ye; Sun Letian;

  • 作者单位

    Chongqing Univ, Sch Software Engn, Chongqing 401331, Peoples R China|Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China|Natl Univ Singapore, Sch Comp, Singapore 117417, Singapore;

    Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China;

    Southwest Univ, Fac Comp & Informat Sci, Chongqing 400715, Peoples R China;

    Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China;

    Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China;

    Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号