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In this paper, we analyze Twitter signals as a medium for user sentiment topredict the price fluctuations of a small-cap alternative cryptocurrency called\emph{ZClassic}. We extracted tweets on an hourly basis for a period of 3.5weeks, classifying each tweet as positive, neutral, or negative. We thencompiled these tweets into an hourly sentiment index, creating an unweightedand weighted index, with the latter giving larger weight to retweets. These twoindices, alongside the raw summations of positive, negative, and neutralsentiment were juxtaposed to $\sim 400$ data points of hourly pricing data totrain an Extreme Gradient Boosting Regression Tree Model. Price predictionsproduced from this model were compared to historical price data, with theresulting predictions having a 0.81 correlation with the testing data. Ourmodel's predictive data yielded statistical significance at the $p < 0.0001$level. Our model is the first academic proof of concept that social mediaplatforms such as Twitter can serve as powerful social signals for predictingprice movements in the highly speculative alternative cryptocurrency, or"alt-coin", market.

Sentiment-Based Prediction of Alternative Cryptocurrency Price Fluctuations Using Gradient Boosting Tree Model