CAJ | 학술논문

针对情感分类中采用单一特征分类精度不高的问题，提出多特征加权的分类算法：根据扩展的情感词典计算每个词的情感倾向度，经CHI特征选择后，根据情感词的极性强度调整贝叶斯分类模型中该词的正负后验概率，在原值的基础上加上极性强度影响值。实验将该方法和其他3种单特征选择方法在酒店、影视等语料上的分类精度进行了对比，分类精度得到提升。实验结果表明，将词语的情感倾向度的特征融入到分类器中方法，在有效提高情感倾向性分类精度的同时降低了特征维数。
침대정감분류중채용단일특정분류정도불고적문제，제출다특정가권적분류산법：근거확전적정감사전계산매개사적정감경향도，경CHI특정선택후，근거정감사적겁성강도조정패협사분류모형중해사적정부후험개솔，재원치적기출상가상겁성강도영향치。실험장해방법화기타3충단특정선택방법재주점、영시등어료상적분류정도진행료대비，분류정도득도제승。실험결과표명，장사어적정감경향도적특정융입도분류기중방법，재유효제고정감경향성분류정도적동시강저료특정유수。
In the traditional classification method,only one feature was considered,that was not good enough for the precision.In order to improve the precision,a classification method based on integrated features was provided.First, the emotional tendency value of one word was calculated according to an extended sentiment dictionary;then after the CHI selection,the weights of the positive and negative emotion word posterior probability in the Bayesian model were adjusted acrodding to its tendency value.In the experiments,four kinds of corpus such as hotel and movie reviews were used,compared with other three methods,the integrated features method was better.The results showed the precision of classification was improved and the dimension of the feature was reduced.