Cognitive Computation

, Volume 10, Issue 1, pp 136–155 | Cite as

Detecting Multiple Coexisting Emotions in Microblogs with Convolutional Neural Networks

  • Shi Feng
  • Yaqi Wang
  • Kaisong Song
  • Daling Wang
  • Ge Yu


Analyzing human sentiments and emotions is a critical problem in cognitive computing. One fundamental task of sentiment analysis is to infer the sentiment polarity or emotion category of subjective text, such as microblogs. Most existing methods treat sentiment classification as a type of single-label supervised learning problem that classifies a microblog according to sentiment polarity or a single-labeled emotion. However, multiple fine-grained emotions may coexist in a single tweet or sentence of a microblog. We regard emotion detection in microblogs as a multi-label classification problem. First, we develop a graph-based algorithm to automatically build emotion lexicons, which are further utilized to construct distant-supervised corpora from massive microblog datasets. Then, a ranking-based multi-label convolutional neural network model (RM-CNN) that considers the order and relevance of labels is proposed to address emotion detection in microblogs. The RM-CNN model is pre-trained using the distant-supervised corpus and then fine-tuned using specific training data without the need for any manually designed features. Extensive experiments on two real-world datasets demonstrate substantial improvements of our proposed RM-CNN model over the state-of-the-art baseline methods in terms of multi-label classification metrics. We propose an effective RM-CNN model with a distant-supervised learning framework for detecting multiple coexisting emotions in the short text of microblogs.


Emotion classification Convolutional neural network Multi-label learning Sentiment analysis Microblogs 


Funding Information

This study was funded by the National Natural Science Foundation of China (61370074, 61402091) and the Fundamental Research Funds for the Central Universities of China under Grant N140404012.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Shi Feng
    • 1
  • Yaqi Wang
    • 1
  • Kaisong Song
    • 1
  • Daling Wang
    • 1
  • Ge Yu
    • 1
  1. 1.School of Computer Science and EngineeringNortheastern UniversityShenyangChina

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