When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation

<div><p>Bitcoin is an online currency that is used worldwide to make online payments. It has consequently become an investment vehicle in itself and is traded in a way similar to other open currencies. The ability to predict the price fluctuation of Bitcoin would therefore facilitate future investment and payment decisions. In order to predict the price fluctuation of Bitcoin, we analyse the comments posted in the Bitcoin online forum. Unlike most research on Bitcoin-related online forums, which is limited to simple sentiment analysis and does not pay sufficient attention to note-worthy user comments, our approach involved extracting keywords from Bitcoin-related user comments posted on the online forum with the aim of analytically predicting the price and extent of transaction fluctuation of the currency. The effectiveness of the proposed method is validated based on Bitcoin online forum data ranging over a period of 2.8 years from December 2013 to September 2016.</p></div

Example of deep learning data set.

<p>The z-score (, where and represent the mean and standard deviation for every date, respectively) of data for the previous 12 days (<i>t</i> = 12) was used as the values.</p

Example of deep learning data set.

<p>The z-score (, where and represent the mean and standard deviation for every date, respectively) of data for the previous 12 days (<i>t</i> = 12) was used as the values.</p

Statistical significance (<i>p</i>-values) of bivariate Granger causality correlation between Bitcoin transaction and concept of forum opinions.

<p>Statistical significance (<i>p</i>-values) of bivariate Granger causality correlation between Bitcoin transaction and concept of forum opinions.</p

Ten topics generated by the Bitcoin forum documents.

<p>Ten topics generated by the Bitcoin forum documents.</p

Concept for building the workflow.

<p>Concept for building the workflow.</p

Pearson Correlation Coefficient result.

<p>Pearson Correlation Coefficient result.</p

Summary of crawled data.

<p>Summary of crawled data.</p

Example of a deep learning dataset.

<p>The z-score for data from the previous 20 days was used as the values A–J, which indicate the value of the sum of forum opinion on a given date. V–Z denote formal data values (number of topics, sum of replies, sum of views, Google Trends value, and Wikipedia page views) on a given date.</p

Predicting Virtual World User Population Fluctuations with Deep Learning

<div><p>This paper proposes a system for predicting increases in virtual world user actions. The virtual world user population is a very important aspect of these worlds; however, methods for predicting fluctuations in these populations have not been well documented. Therefore, we attempt to predict changes in virtual world user populations with deep learning, using easily accessible online data, including formal datasets from Google Trends, Wikipedia, and online communities, as well as informal datasets collected from online forums. We use the proposed system to analyze the user population of EVE Online, one of the largest virtual worlds.</p></div