计算机科学 ›› 2018, Vol. 45 ›› Issue (8): 213-217.doi: 10.11896/j.issn.1002-137X.2018.08.038
曾蒸1, 李莉2, 陈晶3
ZENG Zheng1, LI Li2, CHEN Jing3
摘要: 对商品、电影等的评论的体现人们对商品的喜好程度,从而为意向购买该商品的人提供参考,也有助于商家调整橱窗货品以取得最大利润。近年来,深度学习在文本上强大的表示和学习能力为理解文本语义、抓取文本所蕴含的情感倾向提供了极好的支持,特别是深度学习中的长短记忆模型(Long Short-Term Memory,LSTM)。评论是一种时序数据形式,通过单词前向排列来表达语义信息。而LSTM恰好是时序模型,可以前向读取评论,并把它编码到一个实数向量中,该向量隐含了评论的潜在语义,可以被计算机存储和处理。利用两个LSTM模型分别从前、后两个方向读取评论,从而获取评论的双向语义信息;再通过层叠多层双向LSTM来达到获取评论深层特征的目的;最后把这个模型放到一个情感分类模型中,以实现情感分类任务。实验证明,该模型相对基准LSTM取得了更好的实验效果,这表示双向深度LSTM能抓取更准确的文本信息。将双向深度LSTM模型和卷积神经网络(Convolutional Neural Network,CNN)进行实验对比,结果表明双向深度LSTM模型同样取得了更好的效果。
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