How Can Social Media Predict the Volatile Patterns of Cryptocurrency? | AI News | Intel Software


[MUSIC PLAYING] Ever wonder if social media
can help make accurate investment predictions in
currency trading platforms? Well, the young
genius Teju Tadi did. I’m David Shaw. And you’ll find out how in
this edition of AI News. Tadi launched a
project in May 2017 that applies deep learning
techniques to cryptocurrencies. It analyzes the correlation
between trader sentiments and their market value. Tadi says, “Many firms
in the equities space have been employing techniques
which make key investment decisions based on social
media data and news headlines. There are algorithms that make
decisions to instantaneously buy a certain equity as soon
as positive news is released faster than any
one person could. The same methodology, I
thought, could be applied to the cryptocurrency space.” The theory of herd instinct can
help explain this phenomenon. It’s the idea that people
tend to act the same way as the majority around them. Tadi believes that by analyzing
Reddit posts, headlines, and tweets, he can really gauge
the sentiment of the public. Rather than using a Recurrent
Neural Network, or RNN, to analyze the
verbiage of posts, Tadi decided to go with
RNTNs, as they are better at recognizing semantics. RNTNs, Tadi said, are great at
considering syntactical order. What does this young
genius plan to do next with his latest innovation? He shares that his
next step is to develop an AI cryptocurrency training
bot that can consider trader sentiment while also being
able to purchase and sell the currency for a profit. Inspired yet? Learn more about Tadi’s
innovation at the links provided. Thanks for watching AI News. We’ll see you next week.

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