摘要
随着互联网技术的快速发展,网络舆情成为舆情系统中重要的组成部分.网络舆情除具备一般舆情系统的特点外,还具有突发及匿名等特点,将现有的基于语义词典的分析方法和基于机器学习的分析方式相结合并验证其有效性.实验结果表明,该方法较单一方法分析准确率更高.
With the rapid development of Internet technology,network public opinion has become an important component of public opinion system.Besides the general characteristics of the public opinion system,the network public opinion system also has the sudden and anonymous characteristics.The paper combines existing analysis methods based on semantic dictionary and machine learning and attempts to verify their effectiveness by experiment.The results show that the method has higher analysis accuracy than single method.
作者
吴宁
尚坡利
彭琳茹
WU Ning SHANG Po-li PENG Lin-ru(l. College of Electrical Engineering, Lanzhou Institute of Technology, Lanzhou 730050, China Electronic and Electrical Engineering Department, Lanzhou Petrochemical Polytechnic, Lanzhou 730060, China College of Electronic Information Engineering, Lanzhou Institute of Technology, Lanzhou 730050, China)
出处
《兰州工业学院学报》
2017年第5期65-68,共4页
Journal of Lanzhou Institute of Technology
基金
甘肃省自然科学基金(1506RJZA057)
甘肃省青年科技基金计划(1606RJYA271)
关键词
舆情
情感倾向
本体
机器学习
public opinion
emotional tendency
ontology
machine learning
