My project dissects the user behavior and content of Twitter to investigate the elements having the strongest correlation to changes in Bitcoin market value. In it, a body of 100,000 tweets over a period of one week are analyzed, and volume data of both tweet generation and user interactions are combined with sentiment analysis to create a series of interactive exploratory data visualizations. The visualizations present comparisons of different methods of user interactions, including retweets, likes, and comments. The digital tool allows filtering to remove data that may be deemed uninfluential before aggregated sentiment analysis takes place. During sentiment analysis, weighted models allow for the balance to be shifted to give preference to the opinions of users with a high follower count or messages with a high level of interaction. As a final stage, time delay between online discussions and Bitcoin trading activity is explored and accounted. Rather than provide definitive answers, the project invites the user to explore the vast array of filtering, weighting, and offset models to form their own conclusions and observations.