计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 93-97.
张璐, 沈忱林, 李寿山
ZHANG Lu, SHEN Chen-lin, LI Shou-shan
摘要： 情绪分析是自然语言处理领域的一个研究热点,其通过分析人们发布的文本推测人们的主观感受。情绪分类是情绪分析中的一个基本任务,旨在判断一个文本的情绪类别。对情绪分类来说,词语的表示具有决定性的作用。许多现有的词向量学习算法只对词语的上下文语义信息进行建模,而忽略了词语的情绪信息,这样会导致上下文相似但情绪相反的词语有相似的词向量。为了解决该问题,通过构建一个由两个基本网络(即文档-词网络和情绪图标-词网络)组成的异构网络来学习情绪特定的词向量。最后,在标注样本上训练一个LSTM分类器。实验结果表明了所提情绪特定词向量学习算法的有效性。
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