Yulan Chen
ABSTRACT
User emotion modeling is a vital problem of social media analysis. In previous studies, content and topology information of social networks have been considered in emotion modeling tasks, but the inflence of current emotion states of other users was not considered. We define emotion influence as the emotional impact from user’s friends in social networks, which is determined by both network structure and node attributes (the features of friends). In this paper, we try to model the emotion influence to help analyze user’s emotion. The key challenges to this problem are: 1) how to combine content features and network structures together to model emotion influence; 2) how to selectively focus on the major social network information related to emotion influence. To tackle these challenges, we propose an attention-based graph convolutional recurrent network to bring in emotion influence and content data. Firstly, we use an attention-based graph convolutional network to selectively aggregate the features of the user’s friends with specific attention. Then an LSTM model is used to learn user’s own content features and emotion influence. The model we proposed is more capable of quantifying the emotion influence in social networks as well as combining them together to analyze the user emotion status. We conduct emotion classification experiments to evaluate the effectiveness of our model on a real world dataset called Sina Weibo1. Results show that our model outperforms several state-of-the-art methods.
- Damian Borth, Tao Chen, Rongrong Ji, and Shih-Fu Chang. 2013. Sentibank: large-scale ontology and classifiers for detecting sentiment and emotions in visual content. In Proceedings of the 21st ACM international conference on Multimedia. ACM, 459–460.Google Scholar
Digital Library
- Meeyoung Cha, Alan Mislove, and Krishna P. Gummadi. 2009. A measurement-driven analysis of information propagation in the flickr social network. 721–730.Google Scholar
- Lorenzo Coviello, Yunkyu Sohn, Adam D. I. Kramer, Cameron Marlow, Massimo Franceschetti, Nicholas A. Christakis, and James H. Fowler. 2014. Detecting Emotional Contagion in Massive Social Networks. 9, 3 (2014), e90315.Google ScholarCross Ref
- Paul Ekman. 1992. An argument for basic emotions. Cognition & emotion 6, 3-4 (1992), 169–200.Google Scholar
- Rui Fan, Jichang Zhao, Yan Chen, and Ke Xu. 2014. Anger is more influential than joy: Sentiment correlation in Weibo. PloS one 9, 10 (2014), e110184.Google ScholarCross Ref
- Emilio Ferrara and Zeyao Yang. 2015. Measuring emotional contagion in social media. PloS one 10, 11 (2015), e0142390.Google ScholarCross Ref
- Amit Goyal, Francesco Bonchi, and Laks V. S Lakshmanan. 2010. Learning influence probabilities in social networks. 241-250 (2010), 241–250.Google Scholar
- William L Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. (2017).Google Scholar
- Thomas N Kipf and Max Welling. 2016. Semi-Supervised Classification with Graph Convolutional Networks. (2016).Google Scholar
- Adam DI Kramer, Jamie E Guillory, and Jeffrey T Hancock. 2014. Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences 111, 24(2014), 8788–8790.Google ScholarCross Ref
- Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In Proceedings of the 31st International Conference on Machine Learning (ICML-14). 1188–1196.Google ScholarDigital Library
- Chengxin Li, Huimin Wu, and Qin Jin. 2014. Emotion classification of chinese microblog text via fusion of bow and evector feature representations. In Natural Language Processing and Chinese Computing. Springer, 217–228.Google Scholar
- Franco Manessi, Alessandro Rozza, and Mario Manzo. 2017. Dynamic Graph Convolutional Networks. (2017).Google Scholar
- George A. Miller. 1995. WordNet: A Lexical Database for English. Communications of the Acm 38, 11 (1995), 39–41.Google ScholarDigital Library
- Gerald Schoenewolf. 1990. Emotional contagion: Behavioral induction in individuals and groups.Modern Psychoanalysis(1990).Google Scholar
- Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, and Xavier Bresson. 2016. Structured Sequence Modeling with Graph Convolutional Recurrent Networks. (2016).Google Scholar
- Xingjian Shi, Zhourong Chen, Hao Wang, Wang Chun Woo, Wang Chun Woo, and Wang Chun Woo. 2015. Convolutional LSTM Network: a machine learning approach for precipitation nowcasting. In International Conference on Neural Information Processing Systems. 802–810.Google Scholar
- Parag Singla and Matthew Richardson. 2008. Yes, there is a correlation:-from social networks to personal behavior on the web. In Proceedings of the 17th international conference on World Wide Web. ACM, 655–664.Google ScholarDigital Library
- Duyu Tang, Bing Qin, Ting Liu, and Zhenghua Li. 2013. Learning sentence representation for emotion classification on microblogs. In Natural Language Processing and Chinese Computing. Springer, 212–223.Google Scholar
- Jie Tang, Tiancheng Lou, and Jon Kleinberg. 2012. Inferring social ties across heterogenous networks. In Proceedings of the fifth ACM international conference on Web search and data mining. ACM, 743–752.Google ScholarDigital Library
- Jie Tang, Yuan Zhang, Jimeng Sun, Jinhai Rao, Wenjing Yu, Yiran Chen, and Alvis Cheuk M Fong. 2012. Quantitative study of individual emotional states in social networks. IEEE Transactions on Affective Computing 3, 2 (2012), 132–144.Google ScholarDigital Library
- Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2017. Graph Attention Networks. (2017).Google Scholar
- Xiaohui Wang, Jia Jia, Jie Tang, Boya Wu, Lianhong Cai, and Lexing Xie. 2015. Modeling emotion influence in image social networks. IEEE Transactions on Affective Computing 6, 3 (2015), 286–297.Google ScholarDigital Library
- Yang Yang, Jia Jia, Boya Wu, and Jie Tang. 2016. Social Role-Aware Emotion Contagion in Image Social Networks.. In AAAI. 65–71.Google Scholar
- Mohamed Yassine and Hazem Hajj. 2010. A framework for emotion mining from text in online social networks. In Data Mining Workshops (ICDMW), 2010 IEEE International Conference on. IEEE, 1136–1142.Google ScholarDigital Library
- Shumei Zhang, Jia Jia, and Yishuang Ning. 2017. Inferring emotions from heterogeneous social media data: A Cross-media Auto-Encoder solution. In Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on. IEEE, 2891–2895.Google ScholarDigital Library
- Jiang Zhong and ST Deng. 2012. Classification approach of Chinese texts sentiment based on integrated features. Application Research of Computers 29 (2012), 98–100.Google Scholar
Recommendations
Modeling of emotion elicitation conditions for a cognitive-emotive architecture
AbstractTo cater to the need of embodying emotional behavior in an autonomous agent, there is a need for modeling computationally apt definitions of emotions. A number of emotion theories have been developed that provide an understanding of ...
Rumor Detection in Social Network via Influence Based on Bi-directional Graph Convolutional Network
Web Information Systems Engineering – WISE 2022AbstractNowadays, social media has become a convenient and prevalent platform for users to communicate with others and share their opinions publicly. In the meantime, due to the rapid growth of social media, the circulation of untrue and irresponsible ...
Attentive Recurrent Social Recommendation
SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information RetrievalCollaborative filtering(CF) is one of the most popular techniques for building recommender systems. To alleviate the data sparsity issue in CF, social recommendation has emerged by leveraging social influence among users for better recommendation ...
Comments