[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119448-en":3,"doc-seo-119448-105":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":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},119448,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning Models for Assisting Twitch Streamers - A Thesis","Online entertainment is rapidly evolving into a new frontier for AI and machine learning, with livestreaming platforms enabling streamers to entertain audiences via gameplay and real-time commentary. New or inexperienced streamers may struggle to deliver meaningful narration while managing the game itself. This thesis builds a machine learning approach for detecting events in League of Legends and generating timely commentary. A labeled events dataset trains an event-detection model, then modern tools such as ChatGPT and text-to-speech (Fake You voices) produce audience-facing commentary. Model accuracy and commentary relevance validate the approach, supporting future research across other games and streaming activities.","MACHINE LEARNING MODELS FOR ASSISTING TWITCH STREAMERS  \nA Thesis  \nPresented to the  \nFaculty of  \nCalifornia State Polytechnic University, Pomona  \nIn Partial Fulfillment  \nOf the Requirements for the Degree  \nMasters in Science  \nIn  \nComputer Science  \nBy  \nNoah Renella  \n2023  \nSIGNATURE PAGE  \nTHESIS: MACHINE LEARNING MODELS FOR  \nASSISTING TWITCH STREAMERS  \nAUTHOR: Noah Renella  \nDATE SUBMITTED: Spring 2023  \nDepartment of Computer Science  \nMarkus Eger  \nThesis Committee Chair Professor of Computer Science  \nEricsson Santana Marin  \nProfessor of Computer Science  \nJohn Korah  \nProfessor of Computer Science  \n_________________________________________  \n_________________________________________  \n_________________________________________  \nABSTRACT  \nThe world of online entertainment is constantly evolving and is becoming a new frontier for AI and machine learning. One such evolution of online entertainment is the rise of livestreaming platforms and the streamers on them. Streamers provide entertainment to an audience by broadcasting and commentating on their own gameplay. However, new, or inexperienced Streamers can find it challenging to provide meaningful commentary while focusing on the game.  \nBy creating a machine learning model capable of detecting events and providing commentary using modern technologies, a streamer can focus on growing other aspects of their stream while still providing entertainment to their audience. I do this by creating a dataset of labelled events for the game “League of Legends” and training a model to detect these events. Once the event is detected I use modern technologies such as ChatGPT and Fake You voices to present the commentary to the audience. I then validate the results by getting the accuracy of the model and by checking the relevancy of commentary it is providing.  \nThis provides a framework for future research into the field of AI in online entertainment and allows for testing with other games or streaming activities.  \nTABLE OF CONTENTS  \nSIGNATURE PAGE ........................................................................................................... ii  \nABSTRACT....................................................................................................................... iii  \n[LIST OF TABLES ............................................................................................................. vi](LIST OF TABLES ............................................................................................................. vi)  \n[LIST OF FIGURES ...............................](LIST OF FIGURES ...............................)........................................................................... vii  \nLIST OF LISTINGS ........................................................................................................ viii  \nIntroduction ......................................................................................................................... 1  \nBackground ......................................................................................................................... 2  \nRelated Work ................................................................................................................... 2  \nGame Platform ................................................................................................................ 3  \nTensorFlow & TensorFlow Lite ...................................................................................... 4  \nChatGPT .......................................................................................................................... 5  \nFake You .......................................................................................................................... 5  \nPipeline ............................................................................................................................ 6  \nData Collection and Processing ..........................................","cbCaigfMllqA1BkK","https://ap.wps.com/l/cbCaigfMllqA1BkK","pdf",1775276,1,48,"English","en",105,"# Introduction\n## Background\n## Related Work\n## Game Platform\n# TensorFlow & TensorFlow Lite\n# ChatGPT\n# Fake You\n# Pipeline\n## Data Collection and Processing\n## Image Collection\n## Image Annotation\n# Machine Learning Algorithm\n## Model Training\n# Stream Assisting System\n## Event Detection Algorithm\n## Commentary Generation Algorithm\n## Commentary Audio Algorithm\n# Results\n## Experiment Setup\n## Quantitative Evaluation\n## Qualitative Evaluation\n# Conclusion\n## Future Work\n# References\n# Appendix","[{\"question\":\"What problem does the thesis address for Twitch streamers?\",\"answer\":\"It addresses how inexperienced streamers can find it difficult to provide meaningful commentary while focusing on gameplay.\"},{\"question\":\"How does the proposed system create commentary for viewers?\",\"answer\":\"It uses a trained event-detection model for League of Legends, then applies ChatGPT and Fake You voices to produce and deliver commentary.\"},{\"question\":\"How is the approach evaluated?\",\"answer\":\"Evaluation includes quantitative accuracy of the event-detection model and qualitative checks of the relevance of the generated commentary.\"}]","Machine Learning Models for Assisting Twitch Streamers - 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