Sentiment analysis, which is a notably method in text mining, can be used to extract the sentiment from people’s opinion. It then provides us with valuable perception on a topic from the public’s attitude, which create more opportunities for deeper analysis and prediction.
The thesis aims to investigate public’s sentiment towards Bitcoin through analyzing 10 million Bitcoin related tweets and assigning sentiment points on tweets, then using sentiment fluctuation as a factor to predict future crypto fluctuation. Price prediction is achieved by using a machine learning model called Recurrent Neural Network which automatically learns the pattern and generate following results with memory. The analysis revels slight connection between sentiment and crypto currency and the Neural Network model showed a strong connection between sentiment score and future price prediction.
Sports analytics is a growing field that attempts to showcase interesting aspects of a sport with the use of modern technology and machine learning techniques. This thesis will demonstrate how the NBA has progressed in the past decade by comparing the performance have five teams (SAS, OKC, PHO, MIN, and SAC). It will also provide key insight on what an NBA team should focus on to build an optimized NBA team composition, which will better their performance in the league, which will improve their chances of making into the playoffs. These teams were chosen after conducting extensive analysis on all NBA teams. These five teams were chosen because of the variability in performance (two successful and three less successful teams). Two successful teams, SAS and OKC, and three less successful teams, PHO, MIN, and SAC, were chosen to exemplify the different approaches of teams in the NBA and to distinguish what an NBA team should consider build an optimized team composition to better their performance in the league stage.