Abstract
Analyzing and understanding Internet news are important for many applications, such as market sentiment investigation and crisis management. However, it is challenging for users to interpret a massive amount of unstructured text, to dig out its accurate meaning, and to spot noteworthy news events. To overcome these challenges, we propose a novel visualization-driven approach for analyzing news text. We first collect Internet news from different sources and encode sentences into a vector representation suitable for input to a neural network, which calculates a sentiment score, to help detect news event patterns. A subsequent interactive visualization framework allows the user to explore the development of and relationships between Internet news topics. In addition, a method for detecting news events enables users and domain experts to interactively explore the correlations between market sentiment, topic distribution, and event patterns. We use this framework to provide a web-based interactive visualization system. We demonstrate the applicability and effectiveness of our proposed system using case studies involving blockchain news.
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Wu, Y. C.; Liu, S. X.; Yan, K.; Liu, M. C.; Wu, F. Z. OpinionFlow: Visual analysis of opinion diffusion on social media. IEEE Transactions on Visualization and Computer Graphic. Vol. 20, No. 12, 1763–1772, 2014.
Devlin, J.; Chang, M.-W.; Lee, K.; Toutanova, K. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprin. arXiv:1810.04805, 2018.
Gers, F. A.; Schmidhuber, J.; Cummins, F. Learning to forget: Continual prediction with LSTM. In: Proceedings of the 9th International Conference on Artificial Neural Networks, 850–855, 1999.
Blei, D. M.; Ng, A. Y.; Jordan, M. I. Latent dirichlet allocation. Journal of Machine Learning Researc. Vol. 3, 993–1022, 2003.
Liu, X.; Tang, K. Z.; Hancock, J., Han, J. W.; Song, M., Xu, R.; Pokorny, B. A text cube approach to human, social and cultural behavior in the twitter stream. In: Social Computing, Behavioral-Cultural Modeling and Prediction. Lecture Notes in Computer Science, Vol. 7812. Greenberg, A. M.; Kennedy, W. G.; Bos, N. D. Eds. Springer Berlin Heidelberg, 321–330, 2013.
Zhu, M. F.; Chen, W.; Xia, J. Z.; Ma, Y. X.; Zhang, Y. K.; Luo, Y. T.; Huang, Z.; Liu, L. Location2vec: A situation-aware representation for visual exploration of urban locations. IEEE Transactions on Intelligent Transportation System. Vol. 20, No. 10, 3981–3990, 2019.
Yuan, N. J., Zheng, Y.; Xie, X.; Wang, Y. Z.; Zheng, K.; Xiong, H. Discovering urban functional zones using latent activity trajectories. IEEE Transactions on Knowledge and Data Engineerin. Vol. 27, No. 3, 712–725, 2015.
Doumit, S.; Minai, A. Online news media bias analysis using an LDA-NLP approach. In: Proceedings of the International Conference on Complex Systems, 2011.
Liu, B.; Hu, M. Q.; Cheng, J. S. Opinion observer: Analyzing and comparing opinions on the Web. In: Proceedings of the 14th International Conference on World Wide Web, 342–351, 2005.
Oelke, D.; Hao, M.; Rohrdantz, C.; Keim, D. A.; Dayal, U.; Haug, L.-E.; Janetzko, H. Visual opinion analysis of customer feedback data. In: Proceedings of the IEEE Symposium on Visual Analytics Science and Technology, 187–194, 2009.
Morinaga, S.; Yamanishi, K.; Tateishi, K.; Fukushima, T. Mining product reputations on the Web. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 341–349, 2002.
Chen, C. M.; Ibekwe-Sanjuan, F.; SanJuan, E.; Weaver, C. Visual analysis of conflicting opinions. In: Proceedings of the IEEE Symposium on Visual Analytics Science and Technology, 59–66, 2006.
Wu, Y. C.; Wei, F. R.; Liu, S. X.; Au, N., Cui, W. W.; Zhou, H.; Qu, H. OpinionSeer: Interactive visualization of hotel customer feedback. IEEE Transactions on Visualization and Computer Graphic. Vol. 16, No. 6, 1109–1118, 2010.
Wu, Y. C.; Chen, Z. T.; Sun, G. D.; Xie, X.; Cao, N.; Liu, S. X.; Cui, W. StreamExplorer: A multi-stage system for visually exploring events in social streams. IEEE Transactions on Visualization and Computer Graphic. Vol. 24, No. 10, 2758–2772, 2018.
Reuter, T.; Papadopoulos, S.; Petkos, G.; Mezaris, V.; Kompatsiaris, Y.; Cimiano, P.; de Vries, C.; Geva, S. Social event detection at mediaeval 2013: Challenges, datasets, and evaluation. In: Proceedings of the MediaEval Multimedia Benchmark Workshop Barcelona, 2013.
Fernando, T.; Denman, S.; Sridharan, S.; Fookes, C. Soft+hardwired attention: An LSTM framework for human trajectory prediction and abnormal event detection. Neural Network. Vol. 108, 466–478, 2018.
Dork, M.; Gruen, D.; Williamson, C.; Carpendale, S. A visual backchannel for large-scale events. IEEE Transactions on Visualization and Computer Graphic. Vol. 16, No. 6, 1129–1138, 2010.
Zhao, J.; Cao, N.; Wen, Z.; Song, Y. L.; Lin, Y. R.; Collins, C. FluxFlow: Visual analysis of anomalous information spreading on social media. IEEE Transactions on Visualization and Computer Graphic. Vol. 20, No. 12, 1773–1782, 2014.
Nakamoto, S. Bitcoin: A peer-to-peer electronic cash system. 2019. Available at https://git.dhimmel.com/bitcoin-whitepaper/.
Yli-Huumo, J.; Ko, D.; Choi, S.; Park, S.; Smolander, K. Where is current research on blockchain technology? A systematic review. PLoS On. Vol. 11, No. 10, e0163477, 2016.
Yue, X. W.; Shu, X. H.; Zhu, X. Y.; Du, X. N.; Yu, Z. Q.; Papadopoulos, D.; Liu, S. BitExTract: Interactive visualization for extracting bitcoin exchange intelligence. IEEE Transactions on Visualization and Computer Graphic. Vol. 25, No. 1, 162–171, 2018.
Battista, G. D.; Donato, V. D.; Patrignani, M.; Pizzonia, M.; Roselli, V.; Tamassia, R. Bitconeview: Visualization of flows in the bitcoin transaction graph. In: Proceedings of the IEEE Symposium on Visualization for Cyber Security, 1–8, 2015.
Ranshous, S.; Joslyn, C. A.; Kreyling, S.; Nowak, K.; Samatova, N. F.; West, C. L.; Winters, S. Exchange pattern mining in the bitcoin transaction directed hypergraph. In: Financial Cryptography and Data Security. Lecture Notes in Computer Science, Vol. 10323. Brenner, M. et al. Eds. Springer Cham, 248–263, 2017.
McGinn, D.; McIlwraith, D.; Guo, Y. Towards open data blockchain analytics: A Bitcoin perspective. Royal Society Open Scienc. Vol. 5, No. 8, 180298, 2018.
Information on https://www.8btc.com/.
Information on http://www.bitcoin86.com/.
Goldberg, Y.; Levy, O. Word2vec explained: Deriving Mikolov et al.’s negative-sampling word-embedding method. arXiv preprin. arXiv:1402.3722, 2014.
Mousa, A., Schuller, B. Contextual bidirectional long short-term memory recurrent neural network language models: A generative approach to sentiment analysis. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Vol. 1, 1023–1032, 2017.
Yang, Z. C.; Yang, D. Y.; Dyer, C., He, X. D.; Smola, A., Hovy, E. Hierarchical attention networks for document classification. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1480–1489, 2016.
Hoffman, M.; Bach, F. R.; Blei, D. M. Online learning for latent dirichlet allocation. In: Proceedings of the Advances in Neural Information Processing Systems 23, 856–864, 2010.
Van Laarhoven, P. J. M.; Aarts, E. H. L. Simulated annealing. In: Simulated Annealing: Theory and Applications, Vol. 37. Dordrecht: Springer Netherlands, 7–15, 1987.
Sievert, C.; Shirley, K. LDAvis: A method for visualizing and interpreting topics. In: Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, 63–70, 2014.
Bollinger, J. Using bollinger bands. Stocks & Commoditie. Vol. 10, No. 2, 47–51, 1992.
Kailath, T.; Frost, P. An innovations approach to least-squares estimation—Part II: Linear smoothing in additive white noise. IEEE Transactions on Automatic Contro. Vol. 13, No. 6, 655–660, 1968.
Eubank, R. L. Nonparametric Regression and Spline Smoothing. CRC Press, 1999.
Marchand, P.; Marmet, L. Binomial smoothing filter: A way to avoid some pitfalls of least-squares polynomial smoothing. Review of Scientific Instrument. Vol. 54, No. 8, 1034–1041, 1983.
Van der Maaten, L.; Hinton, G. Visualizing data using t-SNE. Journal of Machine Learning Researc. Vol. 9, 2579–2605, 2008.
Acknowledgements
This work was supported by the National Key Research and Development Project of China (No. 2017YFC0804401) and the National Natural Science Foundation of China (No. U1909204). The work was supported by Prof. Wei Chen, who provided suggestions on how to build an interactive visualization system, and Mrs. Liyan Liu, who provided valuable ideas on how to conduct standardized tests.
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Songye Han is currently an undergraduate of Zhejiang University and will receive his B.S. degree in 2020. His research interests lie in visual analytics and natural language processing.
Shaojie Ye is currently an undergraduate of Zhejiang University and will receive his B.S. degree in 2020. His research interests lie in visual analytics and machine learning.
Hongxin Zhang is an associate professor of the State Key Laboratory of CAD & CG, Zhejiang University. He received his Ph.D. degree in applied mathematics from Zhejiang University in 2002. His research interests include geometric modeling, visual analytics, and machine learning.
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Han, S., Ye, S. & Zhang, H. Visual exploration of Internet news via sentiment score and topic models. Comp. Visual Media 6, 333–347 (2020). https://doi.org/10.1007/s41095-020-0178-4
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DOI: https://doi.org/10.1007/s41095-020-0178-4