dc.description.abstract | With the rise of web 2.0, Video Sharing Websites have become one of the several main sites where people spend most of their time. YouTube is one of the most popular Video Sharing Website and becomes the top most-trafficked website in the world. The numbers of video in YouTube have exceeded one billion. In order to allow user find the video which they are interested in. To manage and classify of these huge amounts of video in YouTube become a major task.
In addition to the video which is uploaded by users, YouTube had many User-generated content. Such as description, keyword, title, comment of these video. The User-generated content in YouTube usually with subjective thoughts and containing personal sentiments and opinions. However the factor of sentiment was not the consideration in the study of YouTube classification in the past, so we focus on sentiment classification in YouTube.
In this study, we collect YouTube data and build an ensemble model to classify the video in YouTube. We based on title, description, keyword, comment features. The ensemble model integrates results from the Supervised learning method and Unsupervised learning method.
Our experiments are executed by the data mining tool and performed by a 10-fold cross-validation. Experiment results show that the performance of our ensemble model outperforms each individual classifier and the features we propose can effectively classify the video in YouTube to correct sentiment.
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