面向情绪分类的情绪词向量学习

Emotion-specific word embedding learning for emotion classification

提出了一种面向情绪分类的融合词内部信息和情绪标签的词向量学习方法。在CBOW模型的基础上,引入词内部成分和情绪标签信息,以适应微博情绪表达的不规范,同时丰富词向量的情绪语义。对于输入文本,按照词的TF-IDF权重对词向量进行加权求和,以作为文本向量表示。以上述词向量或文本向量作为情绪分类器的输入,采用机器学习的分类方法(LR、SVM、CNN),验证本文情绪词向量在情绪分类任务上的实验效果。实验表明,情绪词向量与原始CBOW词向量相比,在准确率、召回率、F值等各项指标上都有更好的表现。

We present a method for emotion classification based on word vector learning which considering the inner patterns and emotion labels of words. Based on the CBOW model, we introduce the inner patterns and the emotion label, in order to enrich the emotional semantics of the word vectors. For one input document, according to the TF-IDF weight of the word, we use the weighted linear combination as the text representation. We use the word vectors or text vectors as the input of the emotion classifier, using machine learning classification method （LR, SVM, CNN）, to verify the experimental results in emotion classification task. Experiments show that the presented algorithm performs better than CBOW model.