Emotion-Semantic-Enhanced Neural Network

Abstract

Although sentiment analysis on microblog posts has been studied in depth, sentiment analysis of posts is still challenging because of the limited contextual information that they normally contain. In microblog environments, emoticons are frequently used and they have clear emotional meanings. They are important emotional signals for microblog sentimental analysis. Existing studies typically use emoticons as noisy sentiment labels or similar sentiment indicators to effectively train classifier but overlook their emotional potentiality. We address this issue by constructing an emotional space as a feature representation matrix and projecting emoticons and words into the emotional space based on the semantic composition. To improve the performance of sentimental analysis, we propose a new emotion-semantic-enhanced convolutional neural network (ECNN) model. ECNN can use emoticon embedding as an emotional space projection operator. By projecting emoticons and words into an emoticon space, it can help identify subjectivity, polarity, and emotion in microblog environments. It is more capable of capturing emotion semantic than other models, so it can improve the sentiment analysis performance. The experimental results show that this model consistently outperforms other models on the dataset of several sentiment tasks. This paper provides insights on the design of ECNN for sentimental analysis in other natural language processing tasks.