[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121380-en":3,"doc-seo-121380-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},121380,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Effect of Twitter Sentiment on Stock Price Returns and Prediction Using Machine Learning","This thesis investigates how Twitter sentiment influences stock price returns and evaluates predictive performance using machine learning methods. The study builds on time-series modeling concepts such as ARIMA and volatility modeling with GARCH, and compares sequence learning approaches including RNN, LSTM, and GRU. Sentiment analysis is performed to transform social media signals into modeling inputs, followed by ethical considerations. Experiments include descriptive analysis across multiple companies, leading to results and findings on relationships between sentiment and market behavior.","CALIFORNIA STATE UNIVERSITY, NORTHRIDGE  \nEffect of Twitter Sentiment on Stock Price Returns and Prediction Using Machine Learning  \nA thesis submitted in partial fulfillment of the  \nrequirements for the degree of Master of Science  \nin Computer Science  \nby  \nRaj Kumar Lakoji  \nThe thesis of Raj kumar Lakoji is approved:  \nDr. Katya Mkrtchyan Ph.D.  \nDr. Mahdi Ebrahim Ph.D.  \nDr. Taehyung Wang Ph.D., Chair  \nDate  \nDate  \nDate  \nCalifornia State University, Northridge  \nAcknowledgments  \nI would like to extend my heartfelt thanks to everyone who played a role in the success of my thesis.  \nI am especially grateful to my thesis advisor, Dr. Taehyung Wang, for his priceless mentorship, profound insights, and consistent encouragement during the research process. His expertise and motivation were critical to the realization of this work.  \nI owe a debt of gratitude to my thesis committee members, Dr. Mahdi Ebrahim and Dr. Katya Mkrtchyan, for their valuable critiques and suggestions that greatly enhanced my research.  \nLastly, my deepest thanks go to my family for their constant support, inspiration, and patience throughout my educational journey. Their unwavering love and encouragement have been the pillars of my motivation.  \nTable of Contents  \nSignature Page ...................................................................................................................... ii  \n[List of Tables ....................................................................................................................... vi](List of Tables ....................................................................................................................... vi)  \n[List of Figures...............................](List of Figures...............................).......................................................................................vii  \nAbstract..................................................................................................................................x  \nChapter 1 Introduction .......................................................................................................... 1  \n1.1 Background ................................................................................................................. 1  \n1.1.1 ARIMA (Autoregressive Integrated Moving Average) ..................................... 2  \n1.1.2 GARCH (Generalized Autoregressive Conditional Heteroskedasticity) ........... 2  \n1.1.3 RNN (Recurrent Neural Network) ..................................................................... 2  \n1.1.4 LSTM (Long Short-Term Memory) .................................................................. 3  \n1.1.5 GRU (Gated Recurrent Unit) ............................................................................. 4  \n1.2 Aims and Objectives ................................................................................................... 4  \n1.3 Motivation ................................................................................................................... 5  \n1.4 State of the Art ............................................................................................................ 6  \nChapter 2 Literature Review .................................................................................................. 8  \n2.1 Effects Of Market Performance by Social Media Sentiment.....................................11  \n2.2 Research Gap ............................................................................................................. 13  \nChapter 3 Methodology ......................................................................................................... 15  \n3.1 : Research Outline ....................................................................................................... 15  \n3.2 : Research Design ....................................................................................................... 15  \n3.3 Population ..........................","cbCaid7MxGSIrDy9","https://ap.wps.com/l/cbCaid7MxGSIrDy9","pdf",1686300,1,84,"English","en",105,"# Acknowledgments\n# Abstract\n# Chapter 1 Introduction\n## Background\n## Aims and Objectives\n## Motivation\n## State of the Art\n# Chapter 2 Literature Review\n## Effects of Market Performance by Social Media Sentiment\n## Research Gap\n# Chapter 3 Methodology\n## Research Outline\n## Research Design\n## Population\n## Modelling\n## Sentiment Analysis\n## Ethical Consideration\n# Chapter 4 Results and Findings\n## Introduction\n## Descriptive Statistics and Analysis","[{\"question\":\"What is the main research focus of the thesis?\",\"answer\":\"The thesis focuses on the effect of Twitter sentiment on stock price returns and on predicting stock performance using machine learning.\"},{\"question\":\"Which modeling approaches are discussed as part of the research background?\",\"answer\":\"The work includes ARIMA and GARCH for time-series and volatility modeling, and RNN, LSTM, and GRU for sequence learning approaches.\"},{\"question\":\"How are Twitter signals incorporated into the prediction process?\",\"answer\":\"Twitter sentiment is analyzed to convert social media text into sentiment features, which are then used as inputs in the machine learning modeling and evaluation.\"}]","Effect of Twitter Sentiment on Stock Price Returns and Prediction Using Machine Learning | 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