Analyzing User Comments On YouTube Coding Tutorial Videos

Video coding tutorials enable expert and novice programmers to visually observe real developers write, debug, and execute code. Previous research in this domain has focused on helping programmers find relevant content in coding tutorial videos as well as understanding the motivation and needs of content creators. In this thesis, we focus on the link connecting programmers creating coding videos with their audience. More specifically, we analyze user comments on YouTube coding tutorial videos. Our main objective is to help content creators to effectively understand the needs and concerns of their viewers, thus respond faster to these concerns and deliver higher-quality content. A dataset of 6000 comments sampled from 12 YouTube coding videos is used to conduct our analysis. Important user questions and concerns are then automatically classified and summarized. The results show that Support Vector Machines can detect useful viewers\u27 comments on coding videos with an average accuracy of 77%. The results also show that SumBasic, an extractive frequency-based summarization technique with redundancy control, can sufficiently capture the main concerns present in viewers\u27 comments