CAJ | 학술논문

基于向量空间模型、潜在语义分析等传统文本意见分类模型将文本映射到词汇或语义空间中，侧重于词汇的辨别能力，无法对映像空间给出明确的语义说明，导致其扩展性、准确率等方面的性能受到限制。为此，在人类情感分类理论的基础上，假设文本中的意见表达与人们的情感存在较强的关联，结合词汇语义扩展、特征选择等方法构造3种情感表示模型，把表达人类情感倾向的文本转换到情感空间中，利用情感模型对国外股票论坛信息提取情感特征，构建情感模型，并设计文本意见分类方法。针对实际股票论坛的数据进行实验，结果表明，该分类方法能获得较高的分类准确率。
기우향량공간모형、잠재어의분석등전통문본의견분류모형장문본영사도사회혹어의공간중，측중우사회적변별능력，무법대영상공간급출명학적어의설명，도치기확전성、준학솔등방면적성능수도한제。위차，재인류정감분류이론적기출상，가설문본중적의견표체여인문적정감존재교강적관련，결합사회어의확전、특정선택등방법구조3충정감표시모형，파표체인류정감경향적문본전환도정감공간중，이용정감모형대국외고표론단신식제취정감특정，구건정감모형，병설계문본의견분류방법。침대실제고표론단적수거진행실험，결과표명，해분류방법능획득교고적분류준학솔。
Traditional text classification models and latent semantic analysis model map text to vocabulary text or semantic space,focusing on the ability to distinguish words. But it can not give a clear image of semantic description of the space. As a result,the scalability and accuracy of a text classification algorithm is limited. In this paper,based on the classification of human emotions in psychology, it assumes that there is a strong association between emotions and opinions. It uses lexical semantic extension and feature selection methods to build three emotional representation model, and maps documents which can express human emotions tended to the emotional space. Using emotion features in stock message board obtained by the emotional representation model,it builds the emotion space model and designs opinion classification method. Experimental results on actual stock forum show that the classification accuracy of this method is high.