Exploring Cryptocurrency Sentiments With Clustering Text Mining on Social Media

Abstract

Social media has become a popular communication platform and aggregated mass information for sentimental analysis. As cryptocurrency has become a hot topic worldwide in recent years, this chapter explores individuals' behavior in sharing Bitcoin information. First, Python was used for extracting around one month's set of Tweet data to obtain a dataset of 11,674 comments during a month of a substantial increase in Bitcoin price. The dataset was cleansed and analyzed by the process documents operator of RapidMiner. A word-cloud visualization for the Tweet dataset was generated. Next, the clustering operator of RapidMiner was used to analyze the similarity of words and the underlying meaning of the comments in different clusters. The clustering results show 85% positive comments on investment and 15% negative ones to Bitcoin-related tweets concerning security. The results represent the generally bullish environment of the cryptocurrency market and general user satisfaction during the period concerned.