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An open access, peer-reviewed research paper published on May 12, 2017, by researchers from Korea University and Kangnam University in South Korea has explained a method for predicting the fluctuation in the bitcoin price and transactions based on user opinions posted on online forums.

The paper, entitled “When Bitcoin encounters information in an online forum: Using text mining to analyze user opinions and predict value fluctuation” was written by Young Bin Kim, Jurim Lee, Nuri Park, Jaegul Choo, Jong-Hyun Kim, and Chang Hun Kim. Unlike previous research on Bitcoin forums, which did not give enough attention to noteworthy comments, this new study used an approach that extracted keywords from user comments in an attempt to predict the price and extent of transaction fluctuation based on Bitcoin online forum data ranging over a period of almost three years from December 2013 to September 2016. The researchers then developed a model based on deep learning to predict the bitcoin transaction count and price.

The first step in creating the price predicting model was gathering the data; Bitcoin-related posts on the online forum (bitcointalk.org), daily bitcoin transaction counts, and bitcoin price made up the data that was gathered. Keywords were extracted from the bitcointalk forum data, and these were judged and measured based on forum score ratings. This forum data was collected by data crawlers. The ‘Bitcoin Discussion’ subsection under the ‘Bitcoin’ section was used in this study because this is where comments are posted most actively, but no personally identifiable user data was collected.

Then a document-term matrix was constructed from the 17,381 forum articles and 627,122 user comments collected from the Bitcoin forum. Each article contained five attributes: ‘content’, ‘topic’, ‘comments’, ‘date’, and ‘views’, and each comment contained the ‘content’ and ‘date’ features. Using the ‘date’ field, the researchers split up

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