CAJ | 학술논문

移动互联网的快速发展使得网络上数据量剧增. 如何从纷繁复杂的信息中提取出对人们有用的信息就成为一个亟待解决的课题. 本文提出一种改进的基于情感词典的倾向分析方法,该方法在情感词典中加入领域情感词,并且通过构建辅助词典集来进行辅助分析. 同时给出了一种半自动的词典维护方法来发现新词和更新词典集. 通过对手机领域的评论进行文本级的情感倾向分析,正面情感分析的准确率和召回率达到0.713和0.769,负面情感的准确率和召回率达到0.738和0.706,与传统基于情感词典的方法相比准确率和召回率都有较大提高.
이동호련망적쾌속발전사득망락상수거량극증. 여하종분번복잡적신식중제취출대인문유용적신식취성위일개극대해결적과제. 본문제출일충개진적기우정감사전적경향분석방법,해방법재정감사전중가입영역정감사,병차통과구건보조사전집래진행보조분석. 동시급출료일충반자동적사전유호방법래발현신사화경신사전집. 통과대수궤영역적평론진행문본급적정감경향분석,정면정감분석적준학솔화소회솔체도0.713화0.769,부면정감적준학솔화소회솔체도0.738화0.706,여전통기우정감사전적방법상비준학솔화소회솔도유교대제고.
The volume of data on the network increases remarkably with the rapid development of mobile net-work. How to extract useful information from the complicated information becomes an urgent problem to be solved. An improved semantic orientation analysis was proposed, in which the sentiment dictionary was ex-panded with domain emotional words, and an auxiliary dictionary set was constructed for assistant analysis. To maintain all the dictionaries, a semi-automatic method was also presented. The results in the domain of mobile phone show that the rates of accuracy and recall of positive emotion analysis are 0.713 and 0.769, and the rates of accuracy and those of negative emotion analysis are 0.738 and 0.706, which are improved com-pared with using the traditional method.