山东大学学报(理学版) ›› 2017, Vol. 52 ›› Issue (9): 19-25.doi: 10.6040/j.issn.1671-9352.1.2016.PC4
杨艳,徐冰*,杨沐昀,赵晶晶
YANG Yan, XU Bing*, YANG Mu-yun, ZHAO Jing-jing
摘要: 针对情感分析问题中长句和短句进行情感分类时不同的建模特点,提出了一种基于联合深度学习模型的情感分类方法。该方法融合长短期记忆模型(LSTM)与卷积神经网络(CNN)对影视评论数据进行情感极性判别,该方法采用LSTM模型对上下文进行建模,通过逐词迭代得到上下文的特征向量,采用CNN模型从词向量序列中自动发现特征,并从局部抽取特征后将局部特征整合成全局特征来提高分类效果。所提出的方法在COAE2016评测的任务2的情感极性分类任务中,其系统准确率获得最好结果。
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