基于递归神经网络的推特情绪预测比特币价格

2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS) Pub Date : 2018-10-01 DOI:10.1109/CCCS.2018.8586824

D. Pant, Prasanga Neupane, Anuj Poudel, Anup Pokhrel, Bishnu Lama

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引用次数: 73

摘要

推特上关于比特币的情绪对比特币的整体市值有直接或间接的影响。本研究是通过分析Twitter上的情绪来预测比特币的价格波动，并找到它们之间的关系。从不同的新闻账号来源收集的比特币推文被分为正面和负面情绪。获得的积极和消极推文的百分比与历史价格一起馈送到RNN模型，以预测下一个时间框架的新价格。对推文进行正面和负面两类情绪分类的准确率为81.39%，使用RNN进行整体价格预测的准确率为77.62%。

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Recurrent Neural Network Based Bitcoin Price Prediction by Twitter Sentiment Analysis

The sentiment in Twitter about Bitcoin have direct or indirect influence on overall market value of the Bitcoin. This research is concerned with predicting the volatile price of Bitcoin by analyzing the sentiment in Twitter and to find the relation between them. The tweets of Bitcoin collected from different news account sources are classified to positive or negative sentiments. The obtained percentage of positive and negative tweets are feed to RNN model along with historical price to predict the new price for next time frame. The accuracy for sentiment classification of tweets in two class positive and negative is found to be 81.39 % and the overall price prediction accuracy using RNN is found to be 77.62%.

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2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)

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