[1]李凌霄,李绍滋,曹冬林.基于多情绪源关联模型的中文微博情感分析[J].智能系统学报,2016,11(4):546-553.[doi:10.11992/tis.201605019]
LI Lingxiao,LI Shaozi,CAO Donglin.Emotional multi-source correlation model for chinese micro-blog sentiment analysis[J].CAAI Transactions on Intelligent Systems,2016,11(4):546-553.[doi:10.11992/tis.201605019]
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LI Lingxiao,LI Shaozi,CAO Donglin.Emotional multi-source correlation model for chinese micro-blog sentiment analysis[J].CAAI Transactions on Intelligent Systems,2016,11(4):546-553.[doi:10.11992/tis.201605019]
基于多情绪源关联模型的中文微博情感分析
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
11
期数:
2016年第4期
页码:
546-553
栏目:
学术论文—自然语言处理与理解
出版日期:
2016-07-25
- Title:
- Emotional multi-source correlation model for chinese micro-blog sentiment analysis
- 分类号:
- TP391
- 摘要:
- 社交媒体信息的爆炸式增长，使得依据其对公众舆论情感的分析受到越来越多的关注。与传统文本不同，新浪微博中存在包括情感词、表情、图片和视频等特征在内的多情绪源，本文针对中文社交短文本情感分析中情感词典时效性问题和多情绪源间的关联性问题，提出了一种多情绪源关联模型。该模型考虑微博中的情感词和表情特征及其之间的关联关系，在经典的词典规则投票方法基础上，引入多情绪源以及关联概率，通过概率建模的方式对情感词和表情两类情绪源建立关联模型，实现对微博情感的判别。实验表明，在6 171条微博数据集中，多情绪源关联模型分类准确率达到了85.3%，强于包含情感词和表情的传统投票模型（83.4%）以及包含同类多特征的SVM方法（82.9%）。
- Abstract:
- With the explosion of social media information, sentiment analysis of public opinion is attracting more and more attention. Compared with traditional text, the Sina micro-blog contains a variety of emotional sources, including sentiment words, emoticons, pictures, etc. To solve the problem of the poor timeliness of lexicons in Chinese social short messages and to utilize the correlation between different emotional sources, an emotional multi-source correlation model (EMCM) is proposed to carry out sentiment analysis on a micro-blog. In particular, it takes advantage of the correlation between sentiment words and emoticons. It imports the multi-sources and correlation probabilities, and then builds a correlation model between the two emotional sources, emotional words and emoticons, based on a voting model using sentimental words. Experimental results show that this model achieved an accuracy of 85.3% in 6 171 micro-blogs, higher than either the traditional method based on voting (83.4%) or the SVM method based on similar multi-features (82.9%).
- 参考文献/References:
-
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备注/Memo
收稿日期:2016-05-19。
基金项目:国家自然科学基金项目（61202143，61305061，61402386，61572409）；福建省自然科学基金项目（2013J05100）.
作者简介:李凌霄,男,1990年生,硕士研究生,主要研究方向为跨媒体舆情分析;曹冬林,男,1977年生,博士,厦门大学智能科学与技术系助理教授,主要研究方向为自然语言处理、信息检索、跨媒体舆情分析、计算机视觉、模式识别;李绍滋,男,1963年生,博士,教授,博士生导师,主要研究方向为人工智能及其应用、计算机视觉与机器学习、运动目标检测与识别、跨媒体舆情分析等。主持过多项国家、省市级项目研究,获得省科学技术三等奖两项,发表学术论文200余篇,其中:27篇被SCI检索、171篇EI检索。
通讯作者:曹冬林.E-mail:another@xmu.edu.cn.
更新日期/Last Update:
1900-01-01