Fine-Grained Emotion Classification of Chinese Microblogs Based on Graph Convolution Networks
This work addresses emotion detection in Chinese social media, offering an incremental improvement over existing methods.
The paper tackled fine-grained emotion classification of Chinese microblogs by proposing a syntax-based graph convolution network model with a percentile pooling method, achieving an F-measure of 82.32% and exceeding the state-of-the-art by 5.90%.
Microblogs are widely used to express people's opinions and feelings in daily life. Sentiment analysis (SA) can timely detect personal sentiment polarities through analyzing text. Deep learning approaches have been broadly used in SA but still have not fully exploited syntax information. In this paper, we propose a syntax-based graph convolution network (GCN) model to enhance the understanding of diverse grammatical structures of Chinese microblogs. In addition, a pooling method based on percentile is proposed to improve the accuracy of the model. In experiments, for Chinese microblogs emotion classification categories including happiness, sadness, like, anger, disgust, fear, and surprise, the F-measure of our model reaches 82.32% and exceeds the state-of-the-art algorithm by 5.90%. The experimental results show that our model can effectively utilize the information of dependency parsing to improve the performance of emotion detection. What is more, we annotate a new dataset for Chinese emotion classification, which is open to other researchers.