计算机科学 ›› 2020, Vol. 47 ›› Issue (1): 186-192.doi: 10.11896/jsjkx.181002011
李苑,李智星,滕磊,王化明,王国胤
LI Yuan,LI Zhi-xing,TENG Lei,WANG Hua-ming,WANG Guo-yin
摘要: 评论情感分析是用户生成内容分析的一个研究热点。评论对象的多样性与评论者用语的随意性,导致评论情感分析成为一个非常具有挑战性的任务。现有方法主要通过预先构建情感词表来计算评论的情感极性,但这类方法无法处理同一个词语在不同语境下情感极性存在差异的问题。针对这一问题,文中提出了一种基于注意力的卷积-递归神经网络模型,对评论的情感极性和词语在不同语境下的情感极性进行了建模。通过结合词语在句子中的上下文语境,所提方法可以将注意力集中在主要情感词周围的一个小范围内,并以一种自适应的方式对情感词的情感极性进行计算,提高了词语情感极性判断的准确率,进而提高了短文本的情感极性准确率。与CRNN,CNN以及基于情感词典的方法相比,所提方法在中文数据集(美团评论、党建评论)和英文数据集(亚马逊商品评论数据集)上都达到了更好的效果。
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