CAJ | 학술논문

该文主要研究文本的倾向性分析问题,即判断文本中的论断是正面还是负面的.已有的研究表明,监督分类方法对倾向性分析很有效.但是,多数情况下,已有的标注数据与待判断倾向性的数据不属于同一个领域,此时监督分类算法的性能明显下降.为解决此问题,该文提出一个算法,将文本的情感倾向性与图排序算法结合起来进行跨领域倾向性分析,该算法在图排序算法基础上,利用训练域文本的准确标签与测试域文本的伪标签来迭代进行倾向性分析.得到迭代最终结果后,为充分利用其中倾向性判断较为准确的测试文本来提高整个测试集倾向性分析的精度,将这些较准确的测试文本作为"种子",进一步通过EM算法迭代进行跨领域倾向性分析.实验结果表明,该文提出的方法能大幅度提高跨领域倾向性分析的精度.
해문주요연구문본적경향성분석문제,즉판단문본중적론단시정면환시부면적.이유적연구표명,감독분류방법대경향성분석흔유효.단시,다수정황하,이유적표주수거여대판단경향성적수거불속우동일개영역,차시감독분류산법적성능명현하강.위해결차문제,해문제출일개산법,장문본적정감경향성여도배서산법결합기래진행과영역경향성분석,해산법재도배서산법기출상,이용훈련역문본적준학표첨여측시역문본적위표첨래질대진행경향성분석.득도질대최종결과후,위충분이용기중경향성판단교위준학적측시문본래제고정개측시집경향성분석적정도,장저사교준학적측시문본작위"충자",진일보통과EM산법질대진행과영역경향성분석.실험결과표명,해문제출적방법능대폭도제고과영역경향성분석적정도.
This paper focuses on the opinion analysis of documents, i.e. to determine the overall opinion (e.g., negative or positive) of a given document. Existing studies have shown that, the supervised classification approaches usually perform well in this task. However, in most cases, the performance decreases sharply when the model is transferred from the labeled data domain to a different target domain without labeled data. This raises the issue of cross-domain opinion analysis. In this paper, we propose an iterative algorithm which integrated the opinion orientations of the documents into the graph-ranking algorithm for cross-domain opinion analysis. We apply the graph-ranking algorithm using the accurate labels of old-domain documents as well as the "pseudo" labels of new-domain documents. Over the results of the iterative algorithm, we try to further improve the performance by choosing the test documents whose opinions have been determined more accurately as "seeds", and applying the EM algorithm again for cross-domain opinion analysis. The experiment results indicate that the proposed algorithm could improve the performance of cross-domain opinion analysis dramatically.