融合表情符号图像特征学习的微博情感分类

表情符号作为一种新兴的网络图形化语言,由于能够直观地表达用户的情感和态度,因此在社交平台被广泛使用。现有的利用表情符号进行微博情感分类的研究主要考虑表情符号的文本特征,这样的做法不能很好的捕捉表情符号之间更细粒度的联系,并无法适应表情的不断发展与变化。针对现有研究存在的问题,本文提出了一种基于卷积自编码器的表情图像特征学习的微博情感分类模型。该模型通过卷积自编码器捕捉的表情符号的图像特征,然后将图像的嵌入表达融入到微博的文本特征中,再利用多层感知机进行情感分类。该模型分别在中文和英文微博的数据集上和现有的方法进行了对比,实验证明,本文的方法优于现有的方法,并且在新表情和跨语言环境下的泛化能力更强。

Incorporating Image Features of Emoticons into Microblog Sentiment Classification

Emoticon, as an emerging network graphic language, is widely used on the social platform due to its ability to express the sentiment and attitude of users intuitively. The current studies take emoticons as text features so that they can neither capture more fine grained correlations between emoticons, nor can they adapt to the development and change of emoticons. In order to overcome the above difficulties, we propose an emoticon image feature learning method based on Convolutional Auto Encoder (CAE) for microblog sentiment classification. Our model can learn image features of emoticons by CAE automatically, and such features are incorporated into the embedding representations of microblogs for sentiment classification. We verify the effectiveness of our proposed model on Chinese microblog and twitter datasets, respectively. The experimental results demonstrate that our model outperforms the state of art methods, and the image features learned by our proposed model have stronger generalization ability even with new emoticons in cross language environment.