CAJ | 학술논문

特征选择是舆情监测系统构建的关键步骤之一,好的特征选择方法可以在降低系统时间消耗的同时,提高舆情监测的准确性。针对话题特征提取方法ITF-IDF没有考虑类别信息的缺点,尝试将互信息用于话题特征提取,提出改进的互信息计算方法CMI和DCMI。 CMI方法融入了聚类思想,对新闻报道进行分组验证, DCMI在CMI的基础上,将时间信息量化为时间距离,实现特征互信息的动态更新。实验结果显示,DCMI的性能明显优于基本互信息方法和ITF-IDF方法。
특정선택시여정감측계통구건적관건보취지일,호적특정선택방법가이재강저계통시간소모적동시,제고여정감측적준학성。침대화제특정제취방법ITF-IDF몰유고필유별신식적결점,상시장호신식용우화제특정제취,제출개진적호신식계산방법CMI화DCMI。 CMI방법융입료취류사상,대신문보도진행분조험증, DCMI재CMI적기출상,장시간신식양화위시간거리,실현특정호신식적동태경신。실험결과현시,DCMI적성능명현우우기본호신식방법화ITF-IDF방법。
Feature selection is one of the key steps to build an opinion monitoring system, and a good feature selection method should both reduce time consumption and improve the accuracy of opinion monitoring. Since the existing topic feature selection method ITF-IDF doesn 't consider category information, we attempt to use mutual information for topic feature selection and propose two modified mutual infor-mation methods CMI and DCMI. CMI merges clustering and testifies news stories by group;DCMI quantifies time information as time dis-tance to dynamically update feature mutual information. Experiment results indicate that the performance of DCMI is obviously better than the basic mutual information and ITF-IDF methods.