计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 28-34.doi: 10.11896/jsjkx.191100114
霍丹1, 张生杰2, 万路军1
HUO Dan1, ZHANG Sheng-jie2, WAN Lu-jun1
摘要: 针对现有大多数基于词向量的学习方法只能对词语的语法语境建模,而忽略了词语的情感信息的问题,文中提出了基于上下文的情感词向量训练模型,使用了比较简单的方法来构建情感词向量的学习框架。该模型是能够获取情感的扩展混合模型在句子极性的情感信息和基于上下文级别词向量的融合方法,有效解决了具有相似上下文但相反情感极性的词被映射到相邻的词向量的问题。为验证学习到的情感词向量模型能准确包含情感和上下文词语的语义信息,分别在不同的语言和不同领域的数据集下训练情感词向量,并在词语级别进行了定量实验。结果表明,所提的情感词向量学习模型在情感词向量获取实验中,与传统的词向量学习模型相比,分类效果提升了14个百分点;在词语级别的情感分类实验中,与传统的词袋模型相比,准确性提升了10个百分点,从而也对产品提供商在大量的用户评价中得到有用的信息起到了指导性的作用。
中图分类号:
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