计算机科学 ›› 2021, Vol. 48 ›› Issue (8): 226-233.doi: 10.11896/jsjkx.200700058
所属专题： 自然语言处理 虚拟专题
张瑾, 段利国, 李爱萍, 郝晓燕
ZHANG Jin, DUAN Li-guo, LI Ai-ping, HAO Xiao-yan
摘要： 细粒度情感分析(fine-grained sentiment analysis)是自然语言处理领域的关键问题之一,其通过学习文本的上下文信息来进行特定方面的情感分析,可以帮助用户和商家更好地了解用户评论特定方面的情感。针对基于用户评论的方面级别细粒度情感分析任务,提出了BiGRU-Attention与门控机制(gated mechanisms)相结合的文本情感分类模型。首先,通过整合现有的情感资源,将HOWNET评价情感词典作为种子情感词典,利用SO-PMI算法扩充用户评论情感词典,结合否定词典以及词性信息扩充用户评论情感知识,将用户评价情感知识作为用户评论情感特征信息;其次,引入字词特征与情感特征信息,将它们联合作为模型输入,使用BiGRU对文本进行深层次的特征提取;然后,结合门控机制以及注意力机制,根据获取的方面词信息进一步提取与方面词相关的上下文情感特征信息;最后,在输出层进行文本情感分析,经过softmax获得最终的情感极性。在AIchallenger2018细粒度情感分析中文数据集上,所提模型的Macro_F1_ score值达到了0.7218,性能超过基线系统,获得了较好的实验结果。
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