计算机科学 ›› 2018, Vol. 45 ›› Issue (8): 191-197.doi: 10.11896/j.issn.1002-137X.2018.08.034
温雯1, 陈颖1, 蔡瑞初1, 郝志峰1,2, 王丽娟1
WEN Wen1, CHEN Ying1, CAI Rui-chu1, HAO Zhi-feng1,2, WANG Li-juan1
摘要: 传统的读者情绪分类主要从情感分析的角度出发,着重考量读者评论中体现出来的情感极性。然而现实中,读者评论的缺失有可能影响情绪分类的有效性和及时性。如何融合包括新闻文本和评论在内的多视角信息,对读者情绪进行更加准确的研判,成为了一个具有挑战性的问题。针对这一问题,构建了一种融合多视角信息的多标签隐语义映射模型(Multi-view Multi-label Latent Indexing,MV-MLSI),将不同视角下的文本特征映射到低维语义空间,同时建立特征和标签之间的映射函数,通过最小化重构误差对模型进行求解,并设计了相关算法,从而实现对读者情绪的有效预测。相比于传统模型,该模型不仅可以充分利用多视角的信息,而且考虑了标签之间的相关性。在新闻文本数据集上的实验表明,该方法可以获得更高的准确率和稳定性。
中图分类号:
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