Detecting Multiple Coexisting Emotions in Microblogs with Convolutional Neural Networks
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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.
KeywordsEmotion classification Convolutional neural network Multi-label learning Sentiment analysis Microblogs
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.
This article does not contain any studies with human participants or animals performed by any of the authors.
- 1.Abdel-Hamid O, Deng L, Yu D. Exploring convolutional neural network structures and optimization techniques for speech recognition. Proceedings of 14th annual conference of the international speech communication association; 2013. p. 3366–3370.Google Scholar
- 2.Agrawal R, Gupta A, Prabhu Y, Varma M. Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages. In: Proceedings of the 22nd international world wide web conference. 2013; p. 13–24.Google Scholar
- 3.Bhowmick PK. Reader perspective emotion analysis in text through ensemble based multi-label classification framework. Comput Inf Sci. 2009;2(4):64–74.Google Scholar
- 4.Chiu JPC, Nichols E. Named entity recognition with bidirectional LSTM-CNNs. Trans Assoc Comput Linguist. 2016;4:357–70.Google Scholar
- 7.Feng S, Zhang L, Li B, Wang D, Yu G, Wong K. Is twitter A better corpus for measuring sentiment similarity? In: Proceedings of the 2013 conference on empirical methods in natural language processing. 2013; p. 897–902.Google Scholar
- 8.Goodfellow IJ, Bulatov Y, Ibarz J, Arnoud S, Shet VD. Multi-digit number recognition from street view imagery using deep convolutional neural networks. arXiv:1312.6082 (2013).
- 10.Gui L, Xu R, Lu Q, Du J, Zhou Y. Negative transfer detection in transductive transfer learning. Int J Mach Learn Cyber. Online First. 2017.Google Scholar
- 14.Jijkoun V, Hofmann K. Generating a non-english subjectivity lexicon: relations that matter. In: Proceedings of 12th conference of the european chapter of the association for computational linguistics. 2009; p. 398–405.Google Scholar
- 15.Kalchbrenner N, Grefenstette E, Blunsom P. A convolutional neural network for modelling sentences. In: Proceedings of the 52nd annual meeting of the association for computational linguistics. 2014; p. 655–665.Google Scholar
- 17.Kim Y. Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing. 2014; p. 1746–1751.Google Scholar
- 21.Liu K, Li W, Guo M. Emoticon smoothed language models for twitter sentiment analysis. In: Proceedings of the Twenty-Sixth AAAI conference on artificial intelligence. 2012; p. 1678–1684.Google Scholar
- 24.Ma X, Hovy EH. End-to-end sequence labeling via bi-directional lstm-cnns-crf. In: Proceedings of the 54th Annual meeting of the association for computational linguistics. 2016; p. 1064–1074.Google Scholar
- 26.Manning CD, Schutze H. 2000. Foundations of statistical natural language processing. MIT Press.Google Scholar
- 27.Meng F, Lu Z, Wang M, Li H, Jiang W, Liu Q. Encoding source language with convolutional neural network for machine translation. In: Proceedings of the 53rd Annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing of the asian federation of natural language processing. 2015; p. 20–30.Google Scholar
- 28.Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and their compositionality. In: Proceedings of 27th Annual conference on neural information processing systems. 2013; p. 3111–19.Google Scholar
- 29.Mohammad S, Kiritchenko S, Zhu X. Nrc-canada: building the state-of-the-art in sentiment analysis of tweets. In: Proceedings of the 7th international workshop on semantic evaluation. 2013; p. 321–7.Google Scholar
- 30.Nair V, Hinton GE. Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th international conference on machine learning. 2010; p. 807–814.Google Scholar
- 34.dos Santos CN, Gatti M. Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of the 25th international conference on computational linguistics. 2014; p. 69–78.Google Scholar
- 35.Sintsova V, Pu P. Dystemo: Distant supervision method for multi-category emotion recognition in tweets. ACM, T Intel Syst Tec. 2016;8(1):13,1–13,22.Google Scholar
- 36.Song K, Feng S, Gao W, Wang D, Chen L, Zhang C. Build emotion lexicon from microblogs by combining effects of seed words and emoticons in a heterogeneous graph. In: Proceedings of the 26th ACM conference on hypertext & social media. 2015; p. 283–292.Google Scholar
- 37.Staiano J, Guerini M. Depeche mood: a lexicon for emotion analysis from crowd annotated news. In: Proceedings of the 52nd annual meeting of the association for computational Linguistics. 2014; p. 427–433.Google Scholar
- 39.Sun X, Peng X, Ren F. Detect the emotions of the public based on cascade neural network model. In: 15th IEEE/ACIS International conference on computer and information science. 2016; p. 1–6.Google Scholar
- 40.Tang D, Wei F, Qin B, Zhou M, Liu T. Building large-scale twitter-specific sentiment lexicon: a representation learning approach. In: Proceedings of the 25th international conference on computational linguistics. 2014; p. 172–182.Google Scholar
- 41.Wang J, Yang Y, Mao J, Huang Z, Huang C, Xu W. CNN-RNN: a unified framework for multi-label image classification. In: Proceedings of the 29th IEEE conference on computer vision and pattern recognition. 2016; p. 2285–2294.Google Scholar
- 42.Wang L, Cao Z, de Melo G, Liu Z. Relation classification via multi-level attention cnns. In: Proceedings of the 54th annual meeting of the association for computational linguistics, 2016; p. 1298–1307.Google Scholar
- 44.Wang M, Liu M, Feng S, Wang D, Zhang Y. A novel calibrated label ranking based method for multiple emotions detection in Chinese microblogs. In: Proceedings of the Third CCF conference natural language processing and chinese computing. 2014; p. 238–250.Google Scholar
- 45.Wang Y, Feng S, Wang D, Yu G, Zhang Y. Multi-label Chinese microblog emotion classification via convolutional neural network. In: Proceedings of 18th Asia-Pacific web conference. 2016; p. 567–580.Google Scholar
- 46.Wei Y, Xia W, Huang J, Ni B, Dong J, Zhao Y, Yan S. CNN: single-label to multi-label. arXiv:1406.5726. 2014.
- 48.Wen S, Wan X. Emotion classification in microblog texts using class sequential rules. In: Proceedings of the Twenty-Eighth AAAI conference on artificial intelligence. 2014, p. 187–193.Google Scholar
- 49.Wu F, Song Y, Huang Y. Microblog sentiment classification with contextual knowledge regularization. In: Proceedings of the Twenty-Ninth AAAI conference on artificial intelligence. 2015; p. 2332–2338.Google Scholar
- 51.Ye L, Xu R, Xu J. Emotion prediction of news articles from reader’s perspective based on multi-label classification. In: Proceedings of international conference on machine learning and cybernetics. 2012; p. 2019–2024.Google Scholar
- 52.Zeiler MD. ADADELTA: an adaptive learning rate method. arXiv:1212.5701. 2012.
- 57.Zhang X, Li W, Lu S. Emotion detection in online social network based on multi-label learning. In: Proceedings of 22nd International conference on database systems for advanced applications. 2017; p. 659–674.Google Scholar
- 58.Zhao F, Huang Y, Wang L, Tan T. Deep semantic ranking based hashing for multi-label image retrieval. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2015; p. 1556–1564.Google Scholar