{"id":4446,"date":"2021-11-23T15:49:21","date_gmt":"2021-11-23T20:49:21","guid":{"rendered":"https:\/\/portfolios.cs.earlham.edu\/?p=4446"},"modified":"2021-11-23T15:49:22","modified_gmt":"2021-11-23T20:49:22","slug":"senior-capstone-cryptocurrency-price-prediction-using-sentiment-analysis-and-machine-learning","status":"publish","type":"post","link":"https:\/\/portfolios.cs.earlham.edu\/index.php\/2021\/11\/23\/senior-capstone-cryptocurrency-price-prediction-using-sentiment-analysis-and-machine-learning\/","title":{"rendered":"Senior Capstone: Cryptocurrency Price Prediction using Sentiment Analysis and Machine Learning"},"content":{"rendered":"\n

Predicting cryptocurrency price movements is a well-known problem of interest. In this modern age, social media represents the public sentiment about current events. Twitter especially has attracted a lot of attention from researchers who are studying the public sentiments. Recent studies in natural language processing develop automatic techniques in analyzing sentiment in social media information. This research is directed towards predicting volatile price movement of cryptocurrency by analyzing the sentiment on social media and finding the correlation between them. Machine learning algorithms including support vector machine and linear regression will be used to predict the prices. The most efficient combination of machine learning algorithms and the datasets being used will be determined.<\/p>\n\n\n\n

Software Architecture Diagram <\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Link to video tutorial: https:\/\/youtu.be\/ml4Tc-Xr7bc<\/p>\n\n\n\n

Link to senior paper: https:\/\/drive.google.com\/file\/d\/1S2TUrBGu8VMmVX1iES8osTWCS6J3PfOG\/view?usp=sharing<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Predicting cryptocurrency price movements is a well-known problem of interest. In this modern age, social media represents the public sentiment about current events. Twitter especially has attracted a lot of attention from researchers who are studying the public sentiments. 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