计算机科学 ›› 2019, Vol. 46 ›› Issue (7): 172-179.doi: 10.11896/j.issn.1002-137X.2019.07.027
韩慧1,王黎明1,柴玉梅1,刘箴2
HAN Hui1,WANG Li-ming1,CHAI Yu-mei1,LIU Zhen2
摘要： 为了有效实现评论文本的情感倾向性预测,在深度森林模型的基础上提出一种基于强化表征学习的深度森林算法BFDF(Boosting Feature of Deep Forest)来对文本进行情感分类。首先,提取二元特征与情感语义概率特征;其次,对二元特征中的评价对象做聚类处理以及特征融合;然后,改进深度森林级联层的表征学习能力,避免特征信息逐渐削减;最后,将AdaBoost方法融入到深度森林,使深度森林注意到不同特征的重要性,进而得到改进的模型BFDF。在酒店评论语料集上进行了实验验证,实验结果证明了该方法的有效性。
中图分类号:
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