Institute for Web Science and Technologies · Universität Koblenz - Landau
Institute WeST

Cryptocurrency Price Prediction Using Deep Learning and Social Media Sentiment Analysis

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Azeddine Bouabdallah.

Despite the improvement that cryptocurrency price prediction had in recent years, these studies have not addressed the fact that cryptocurrency fundamentals are different from traditional markets which justifies the obtained results as being far from reliable. Unlike traditional markets, the hash rate of the mining process, the correlation between the cryptocurrency and global financial market, and public awareness are all factors that contribute to the price fluctuations of cryptocurrencies. This thesis aims at introducing a deep learning method for cryptocurrency price prediction on the next trading day using sentiment analysis from social media and search volumes in search engines. The data that will be used in this study is composed of the trading data of cryptocurrencies (time-series, bids, and asks), Twitter posts that will be analyzed, and assigned a weighting score depending on their sentiments and reach, along with search volumes for related terms to cryptocurrencies. Next, an LSTM model will use the collected data to predict the prices on the next trading day along with the confidence level in an effort to increase the overall accuracy. To the best of my knowledge, this is the first approach that uses deep learning with social media sentiments and reaches level along with search volumes to predict cryptocurrency prices.


10.06.21 - 10:15
via Big Blue Button