Composite Kernels for Public Emotion Recognition from Twitter
The Internet has grown into a powerful medium for information dispersion and social interaction that leads to a rapid growth of social media which allows users to easily post their emotions and perspectives regarding certain topics online. Our research aims at using natural language processing and text mining techniques to explore the public emotions expressed on Twitter by analyzing the sentiment behind tweets. In this paper, we propose a composite kernel method that integrates tree kernel with the linear kernel to simultaneously exploit both the tree representation and the distributed emotion keyword representation to analyze the syntactic and content information in tweets. The experiment results demonstrate that our method can effectively detect public emotion of tweets while outperforming the other compared methods.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1474775Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 282
 M. D. Sykora, T. W. Jackson, A. O’Brien, and S. Elayan. "Emotive ontology: Extracting fine-grained emotions from terse, informal messages," International Journal on Computer Science and Information Systems, vol. 8, no. 2, pp. 106-118, 2013.
 O. Miles, et al. "Real-time detection, tracking, and monitoring of automatically discovered events in social media," In Proceedings of the 52nd ACL, 2014.
 Lin, K. H. Y., Yang, C., & Chen, H. H. “What emotions do news articles trigger in their readers,” In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 733-734, ACM, 2007.
 Mostafa Al Masum, S., Prendinger, H., & Ishizuka, M. “Emotion sensitive news agent: An approach towards user centric emotion sensing from the news,” In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, pp. 614-620, IEEE Computer Society, 2007.
 Quan, C., & Ren, F. “A blog emotion corpus for emotional expression analysis in Chinese,” International Journal on Computer Speech & Language, vol. 24, no. 4, pp. 726-749, 2010.
 Pak, A., & Paroubek, P. “Twitter as a corpus for sentiment analysis and opinion mining,” In Proceedings of the Language Resources and Evaluation Conference, vol. 10, 2010.
 Rao, Y., Pang, J., Xie, H., Liu, A., Wong, T. L., Li, Q., & Wang, F. L. "Supervised Intensive Topic Models for Emotion Detection over Short Text," In Proceedings of the Database Systems for Advanced Applications, pp. 408-422, 2017.
 Collins, M., & Duffy, N. “New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron,” In Proceedings of the 40th annual meeting on association for computational linguistics, pp. 263-270, 2002.
 Moschitti, A., “Efficient convolution kernels for dependency and constituent syntactic trees,” In Proceedings of the European Conference on Machine Learning, pp. 318-329, 2006
 Basili, R., Cammisa, M., & Moschitti, A. “Effective use of WordNet semantics via kernel-based learning,” In Proceedings of the Ninth Conference on Computational Natural Language Learning, pp. 1-8, 2005.
 Croce, D., Moschitti, A., & Basili, R. “Structured lexical similarity via convolution kernels on dependency trees,” In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1034-1046, 2011.
 Xiang, B., & Zhou, L. “Improving twitter sentiment analysis with topic-based mixture modeling and semi-supervised training,” In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 2, pp. 434-439, 2014.
 Glorot, X., Bordes, A., & Bengio, Y. “Domain adaptation for large-scale sentiment classification: A deep learning approach,” In Proceedings of the 28th international conference on machine learning, pp. 513-520, 2011.
 Xu, J. M., Jun, K. S., Zhu, X., & Bellmore, A. “Learning from bullying traces in social media,” In Proceedings of the 2012 conference of the North American chapter of the association for computational linguistics: Human language technologies, pp. 656-666, 2012.
 Golbeck, J., Robles, C., & Turner, K. “Predicting personality with social media,” In Proceedings of the CHI'11 extended abstracts on human factors in computing systems, pp. 253-262, 2011.
 Filice, S., Castellucci, G., Croce, D., & Basili, R. “Kelp: a kernel-based learning platform for natural language processing,” In Proceedings of the ACL-IJCNLP 2015 System Demonstrations, pp. 19-24, 2015.
 Manning, Christopher D., and Hinrich Schütze. Foundations of statistical natural language processing. MIT press, 1999.
 Y.-C. Chang, C.-C. Chen, Y.-L. Hsieh, C.-C. Chen, and W.-L. Hsu, “Linguistic template extraction for recognizing reader-emotion and emotional resonance writing assistance,” In Proceedings of the 53rd ACL and the 7th IJCNLP, pp. 775–780, 2015.
 Joachims, T. “Text categorization with support vector machine: Learning with many relevant features,” In Proceedings of the 10th European Conference on Machine Learning, pp. 137–142, 2005.
 Chang, Y. C., Chen, C. C., & Hsu, W. L. “SPIRIT: A tree kernel-based method for topic person interaction detection,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 9, pp. 2494-2507, 2016.
 Manning, C. D., & Raghavan, P. H. Schu tze. “Introduction to Information Retrieval,” 2008.
 Duppada, V., & Hiray, S. “Seernet at emoint-2017: Tweet emotion intensity estimator,” arXiv preprint arXiv:1708.06185, 2017.
 Chen, T., & Guestrin, C. “Xgboost: A scalable tree boosting system,” In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785-794, 2016.
 Miwa, M., Sætre, R., Miyao, Y., & Tsujii, J. I. “Protein–protein interaction extraction by leveraging multiple kernels and parsers,” International journal of medical informatics, vol. 78, no. 12, pp. 39-46, 2009.
 Chang, Y. C., Chen, C. C., & Hsu, W. L. “A composite kernel approach for detecting interactive segments in chinese topic documents,” In Proceedings of the Asia Information Retrieval Societies, pp. 215-226, 2013.
 Zhang, M., Zhang, J., Su, J., & Zhou, G. “A composite kernel to extract relations between entities with both flat and structured features,” In Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, pp. 825-832, 2006.
 Klein, D., & Manning, C. D. “Accurate unlexicalized parsing,”In Proceedings of the 41st annual meeting of the association for computational linguistics, 2003.
 Lai, S., Xu, L., Liu, K., & Zhao, J. “Recurrent Convolutional Neural Networks for Text Classification,” In Proceedings of the AAAI, pp. 2267-2273, 2015.
 Pennington, J., Socher, R., & Manning, C. D., “GloVe: Global Vectors for Word Representation,” https://nlp.stanford.edu/projects/glove/, 2015.