| dc.description.abstract | With YouTube has been becoming the world’s second most popular website, YouTube has been increasing traffic, user volume and revenue. New careers and business models have been emerging, and the business opportunities behind it are very impressive. content providers want to precision marketing, and users also want to find videos that suit them in the vast of videos. In order for users to quickly search for the videos they want, managing and categorizing these huge amounts of videos become the main task.
In addition to uploading video, YouTube also contains other user-generated content, such as video descriptions, keywords, titles, comments, etc., most of which are uploaded by the content provide. While the comments are created by a large number of users, we hope to provide a more objective classification by combining the information generated by content provide and users. In the past, YouTube video classification methods mostly used machine learning methods to analyze text. This paper uses deep learning methods to classify Internet videos into designated emotional categories.
This paper uses text-CNN to extract the local features of the four groups of texts: title, keywords, description, and comments, and then uses Bi-LSTM to analyze longer comment features to more effectively classify YouTube videos into suitable emotions, up to 92.19%. | en_US |