Incorporating Image Features of Emoticons into Microblog Sentiment Classification

Abstract:

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.