摘要
为了识别网络文本的情感倾向性,通过分析文本结构以及情感表达的特点,提出了一种基于K-近邻的文本情感分析方法,将整个文本的情感划分为局部情感和全局情感。建立条件随机场模型,确定文本中的局部情感,通过K-近邻算法计算文本的全局情感。实验结果表明,与传统机器学习方法相比,该方法能细粒度、多层次的分析文本的情感,同时能有效提高情感分析的准确率。
In order to identify polarity of sentiment on web texts, by analyzing the text structure and the characteristics of expressing sentiment in texts, a method based on K-nearest algorithm is proposed. In this method, sentiment of a text is divided into local sentiment and global sentiment. Local sentiment can be determined by conditional random field models, and the K-nearest neighbor algorithm is used to compute global sentiment of the text. Experimemal results show that compared with traditional machine learning methods, this method can analyze sentiment on multi-level and is fine granularity, and can effectively improve accuracy of sentiment analysis.
出处
《计算机工程与设计》
CSCD
北大核心
2012年第3期1160-1164,共5页
Computer Engineering and Design
基金
国家自然科学基金项目(50978030)
陕西省自然科学基金项目(2009-jm8002-1)
高等学校博士学科点专项科研基金项目(20096102120045)
关键词
情感分析
局部情感
全局情感
层次化模型
条件随机场模型
K-近邻算法
analyzing sentiment local sentiment global sentiment hierarchical model conditional random field models K-nearest neighbor algorithm
