LSTM Based Sentiment Analysis for Cryptocurrency Prediction

Author:

Huang Xin,Zhang Wenbin,Tang Xuejiao,Zhang Mingli,Surbiryala Jayachander,Iosifidis Vasileios,Liu Zhen,Zhang Ji

Publisher

Springer International Publishing

Reference8 articles.

1. Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2, 1–8 (2011)

2. Box, G., Jenkins, G., Reinsel, G., Ljung, G.: Time Series Analysis: Forecasting and Control (2016). ISBN 1-118-67502-9. OCLC 915507780

3. Chen, R., Lazer, M.: Sentiment analysis of twitter feeds for the prediction of stock market movement. Stanford Computer Science 229 (2011)

4. Hochreiter, S., Schmidhuber, J.: LSTM can solve hard long time lag problems. In: Proceedings of the 9th International Conference on Neural Information Processing Systems, pp. 473–479 (1996)

5. Hutto, C.J., Gilbert, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International AAAI Conference on Weblogs and Social Media, pp. 216–255 (2014)

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