[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118730-en":3,"doc-seo-118730-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118730,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Application of Sentiment Analysis and Machine Learning Techniques to Predict Daily Cryptocurrency Price Returns - Senior Thesis","This paper examines how social media sentiment related to Bitcoin influences daily price returns for Bitcoin and six other major cryptocurrencies. Using sentiment analysis and machine learning methods, the study evaluates whether investor sentiment provides predictive value for next-day movements. Results show that Bitcoin-related social media sentiment does not significantly improve forecasting accuracy for the six assets. Models that avoid assuming a linear relationship between current and lagged returns, while incorporating sentiment scores, outperform models that enforce linearity assumptions.","Claremont Colleges  \nScholarship @ Claremont  \n\n| CMC Senior Theses | CMC Student Scholarship |\n| --- | --- |\n| 2023\u003Cbr>Application of Sentiment Analysis and Machine Learning Techniques to Predict Daily Cryptocurrency Price Returns\u003Cbr>Edward Wu\u003Cbr>Follow this and additional works at: [https://scholarship.claremont.edu/cmc_theses](https://scholarship.claremont.edu/cmc_theses)\u003Cbr> Part of the Data Science Commons, Econometrics Commons, Finance Commons, Longitudinal Data Analysis and Time Series Commons, and the Statistical Models Commons |  |\n\nRecommended Citation  \nWu, Edward, \"Application of Sentiment Analysis and Machine Learning Techniques to Predict Daily Cryptocurrency Price Returns\" (2023) . CMC Senior Theses. 3220.  \n[https://scholarship.claremont.edu/cmc_theses/3220](https://scholarship.claremont.edu/cmc_theses/3220)  \nThis Open Access Senior Thesis is brought to you by Scholarship@Claremont. It has been accepted for inclusion in this collection by an authorized administrator. For more information, please contact [scholarship@cuc.claremont.edu](scholarship@cuc.claremont.edu).  \nClaremont McKenna College  \nApplication of Sentiment Analysis and Machine Learning Techniques to Predict Daily Cryptocurrency Price Returns  \nSubmitted to  \nProfessor Michael Gelman  \nby  \nEdward Wu  \nfor  \nSenior Thesis  \nFall 2022  \n12/5/22  \n2  \n3  \nAcknowledgments  \nFirst off, I would like to express my gratitude to my thesis advisor, Professor Michael Gelman for his invaluable guidance and feedback during this semester-long journey. Without your support and patience, it would have been difficult for my thesis to come to fruition.  \nAdditionally, I would like to thank Professor Mike Izbicki for providing me with access to historical data of all geolocated tweets dating back to 2017 as well as additional guidance with debugging my code.  \nLastly, I would like to thank my family and friends who provided me with immense emotional support during this tough, grueling journey.  \n4  \nAbstract  \nThis paper examines the effects of social media sentiment relating to Bitcoin on the daily price returns of Bitcoin and other popular cryptocurrencies by utilizing sentiment analysis and machine learning techniques to predict daily price returns. Many investors think that social media sentiment affects cryptocurrency prices. However, the results of this paper find that social media sentiment relating to Bitcoin does not add significant predictive value to forecasting daily price returns for each of the six cryptocurrencies used for analysis and that machine learning models that do not assume linearity between the current day price return and previous daily price returns combined with previous daily sentiment scores were more accurate than machine learning models that assume linearity.  \nKeywords: Cryptocurrency Price Prediction, Machine Learning, Sentiment Analysis, Twitter Sentiment  \n5  \nTable of Contents  \nI. Introduction 6  \nII. Literature Review 7  \n1. Sentiment Analysis 8  \n2. Machine Learning 10  \nIII. Data and Methodologies 12  \n1. Data 12  \n2. Methodologies 15  \nIV. Empirical Results 20  \n1. Baseline Model Results 20  \n2. Experiment Model Results 23  \nV. Conclusion 26  \nVI. Bibliography 28  \nVII. Appendix 30  \nAppendix A: Additional Baseline Model Results 30  \nAppendix B: Additional Experiment Model Results 31  \nAppendix C: Baseline Model Results Figures 32  \nAppendix D: Experiment Model Results Figures 41  \n6  \nI. Introduction  \nAlthough there are various studies focused on predicting stock market price movements based on social media sentiment, research applying sentiment analysis and machine learning techniques to predict cryptocurrency price movements is comparatively sparse. This paper examines the common sentiment analysis and machine learning methods utilized in studies addressing the task of predicting stock prices and assesses whether these methods can be applied to forecasting cryptocurrency prices.  \nIn comparison to the stock market, the ","cbCaitD1o99icbc4","https://ap.wps.com/l/cbCaitD1o99icbc4","pdf",7299510,1,51,"English","en",105,"# I. Introduction\n# II. Literature Review\n## 1. Sentiment Analysis\n## 2. Machine Learning\n# III. Data and Methodologies\n## 1. Data\n## 2. Methodologies\n# IV. Empirical Results\n## 1. Baseline Model Results\n## 2. Experiment Model Results\n# V. Conclusion\n# VI. Bibliography\n# VII. Appendix","[{\"question\":\"What does the thesis investigate regarding Bitcoin and cryptocurrency prices?\",\"answer\":\"The thesis studies whether social media sentiment related to Bitcoin can predict daily price returns for Bitcoin and other popular cryptocurrencies.\"},{\"question\":\"How is social media sentiment incorporated into the prediction approach?\",\"answer\":\"The work applies sentiment analysis techniques, including VADER, to derive daily sentiment signals from social media content and then uses them as inputs to machine learning models.\"},{\"question\":\"What do the empirical results conclude about the predictive value of sentiment?\",\"answer\":\"The results indicate that Bitcoin-related social media sentiment does not add significant predictive value for forecasting daily price returns across the six analyzed cryptocurrencies, and that nonlinear modeling with sentiment inputs performs better than linear-assumption models.\"}]","Application of Sentiment Analysis and Machine Learning Techniques to Predict Daily Cryptocurrency Price Returns - 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