Educational & Industrial Testing Service, San Diego (1971) Mcnair, D., Lorr, M., Droppleman, C.: Profile of mood states. In: The 2013 International Joint Conference on Neural Networks, pp. Lin, Y., Guo, H., Hu, J.: An SVM-based approach for stock market trend prediction. In: Hameurlain, A., Liddle, S.W., Schewe, K.-D., Zhou, X. Li, X., Wang, C., Dong, J., Wang, F., Deng, X., Zhu, S.: Improving stock market prediction by integrating both market news and stock prices. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. Li, R., Wang, S., Deng, H., Wang, R., Chang, K.C.C.: Towards social user profiling: unified and discriminative influence model for inferring home locations. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015) Li, Q., Jiang, L., Li, P., Chen, H.: Tensor-based learning for predicting stock movements. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Renz, M., Shahabi, C., Zhou, X., Chemma, M.A. Huang, Y., Zhou, S., Huang, K., Guan, J.: Boosting financial trend prediction with twitter mood based on selective hidden Markov models. Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. Giacomel, F., Pereira, A.C., Galante, R.: Improving financial time series prediction through output classification by a neural network ensemble. AAAI Press (2015)įama, E.F.: The behavior of stock-market prices. In: Proceedings of the 24th International Conference on Artificial Intelligence, pp. 1415–1425 (2014)ĭing, X., Zhang, Y., Liu, T., Duan, J.: Deep learning for event-driven stock prediction. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, Doha, Qatar, A meeting of SIGDAT, A Special Interest Group of the ACL, 25–29 October 2014, pp. Princeton University Press, New Jersey (2011)ĭing, X., Zhang, Y., Liu, T., Duan, J.: Using structured events to predict stock price movement: an empirical investigation. 2(1), 1–8 (2011)Ĭamerer, C.F., Loewenstein, G., Rabin, M.: Advances in Behavioral Economics. Computer 44(10), 91–94 (2011)īollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. īollen, J., Mao, H.: Twitter mood as a stock market predictor. Twitter: number of monthly active users 2010–2015. Theano is a python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Keras: Deep learning library for theano and tensorflow. The results show that Twitter mood can improve prediction performance under the deep network models, and the Convolutional Neural Network based method performs best on most cases. Extensive experiments over real datasets are carried out to validate the performance of our methods. On the one hand, we utilize a Deep Neural Network of good fitting capability to evaluate and select predictive Twitter moods On the other hand, we use a Convolutional Neural Network to explore temporal patterns of financial data and Twitter moods through convolution and pooling operations. Then, we combine Twitter moods and financial index by Deep Network models, and propose two methods. First, we summarize six-dimensional society moods from Twitter posts based on the profile of mood states Bipolar lexicon expanded by WordNet. In this paper, we exploit Twitter moods to boost next-day financial trend prediction performance based on deep network models. Financial trend prediction is an interesting but also challenging research topic.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |