Fine-grained emotion classification of Chinese microblogs based on graph convolution networks
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Fine-grained emotion classification of Chinese microblogs based on graph convolution networks Yuni Lai1 · Linfeng Zhang1 · Donghong Han1
· Rui Zhou2 · Guoren Wang3
Received: 27 January 2019 / Revised: 5 December 2019 / Accepted: 17 February 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. Keywords Sentiment analysis · Graph convolution network · Chinese microblog · Deep learning · Emotion detection
1 Introduction Sentiment analysis (SA) is a problem belonging to natural language processing (NLP) to detect sentiment polarities of a writer from a piece of text. In the research field of SA, fine-grained emotion classification aims to detect exact types of feelings such as happiness, Yuni Lai and Linfeng Zhang contributed equally to this paper. This article belongs to the Topical Collection: Special Issue on Application-Driven Knowledge Acquisition Guest Editors: Xue Li, Sen Wang, and Bohan Li Donghong Han
[email protected] 1
Northeastern University, Shenyang, 110819, China
2
Swinburne University of Technology, Hawthorn, 3122, Australia
3
Beijing Institute of Technology, Beijing, 100081, China
World Wide Web
sadness, like, anger, disgust, fear, and surprise. Nowadays, more and more Chinese people share their daily feelings on social networks. For example, one of the most popular Chinese microblog platforms, Sina microblog, has its monthly active users increased to 431 million by August 2018 [1]. SA for social network has become a hot topic in recent years, and the analysis results can be widely used in public opinion analysis, psychological research, social events and even political elections [40]. Sentiment analysis (or emotion detection) for English tweets is quite satisfactory in the SemEval-2017 with 48 teams participated [26]. However, there are not as much research in the field of Chinese. One of the key issues in emotion analysis for Chinese tweets is how to understand text with various and complex syntactic stru
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