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Detecting Multiple Coexisting Emotions in Microblogs with Convolutional Neural Networks

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Abstract

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.

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Notes

  1. https://en.wikipedia.org/wiki/Emotion.

  2. http://tcci.ccf.org.cn/conference/2014/index.html.

  3. http://ir.dlut.edu.cn/EmotionOntologyDownload.

  4. http://www.image-net.org/.

  5. http://tcci.ccf.org.cn/conference/2014/pages/page04_eva.html.

  6. http://deeplearning.net/software/theano/.

  7. http://open.weibo.com/.

  8. http://www.keenage.com/html/e_index.html.

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Funding

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.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, No.195 Chuangxin Road, Hunnan District, Shenyang, China

    Shi Feng, Yaqi Wang, Kaisong Song, Daling Wang & Ge Yu

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  1. Shi Feng
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  2. Yaqi Wang
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  3. Kaisong Song
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  5. Ge Yu
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Correspondence to Shi Feng.

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Feng, S., Wang, Y., Song, K. et al. Detecting Multiple Coexisting Emotions in Microblogs with Convolutional Neural Networks. Cogn Comput 10, 136–155 (2018). https://doi.org/10.1007/s12559-017-9521-1

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