CAJ | 학술논문

传统的情感倾向性分析方法主要针对同一领域的文本，对于不同领域的文本，传统方法效果较差。为解决该问题，提出一种基于特征变换的跨领域产品评论倾向性分析方法。通过领域独立词建立源领域和目标领域的领域依赖词之间的关联，将源领域的领域知识迁移到目标领域中，以解决数据分布不同造成的分类器效果下降的问题。使用产品评论文本作为语料进行实验，结果表明，在所有语料上基于支持向量机和逻辑回归方法的平均精度分别为76.61%和76.81%，均高于Baseline算法的平均结果。
전통적정감경향성분석방법주요침대동일영역적문본，대우불동영역적문본，전통방법효과교차。위해결해문제，제출일충기우특정변환적과영역산품평론경향성분석방법。통과영역독립사건립원영역화목표영역적영역의뢰사지간적관련，장원영역적영역지식천이도목표영역중，이해결수거분포불동조성적분류기효과하강적문제。사용산품평논문본작위어료진행실험，결과표명，재소유어료상기우지지향량궤화라집회귀방법적평균정도분별위76.61%화76.81%，균고우Baseline산법적평균결과。
Traditional sentiment analysis methods aim at same domain documents, the performance becomes worse for different domain documents. To solve this problem, this paper presents an opinion analysis method of cross-domain product reviews based on feature transformation. This proposed method builds the relevance of domain dependent words between source domain and target domain via domain independent words so that it can transfer acknowledge from the source domain to the target domain. It solves the classifier performance decreasing problem due to different data distributions. The product reviews are used as a corpus in the experiment. The average accuracies are 76.61% and 76.81% by using the methods of Support Vector Machine(SVM) and logistic regression respectively in all corpora. The results are higher than Baseline algorithm.