Technology is changing the way we consume information and entertainment. YouTube streaming video services provide a discussion function that allows video publishers to know what matters most to the people they want to love their brand. Through comments, video publishers can better understand the audience’s thoughts and even help video publishers improve their video quality. We propsoe a classifier based on machine learning and BERT to automatically detect YouTuber preferences, video preferences, and excitement levels. In order to make high performance of models, we use a pretrained YouTuber embeddings to enhance performance, which is trained in advance based on roughly 175,000 pieces of videos’ comments that contain YouTubers’ name. YouTuber embeddings can capture some of the semantics and character of the relation between YouTubers. Experimental results show that the performances of machine learning-based models with YouTuber embeddings have improved overall accuracy and F1-score on all sentiment classications. The result validates that YouTuber embedding training is significantly helpful when detecting audience sentiment towards YouTubers. On the contrary, BERT model cannot perfectly deal with the polarity classificational tasks when using YouTubers embeddings. However, the BERT model construction is more suitable for addressing multi-dimensional classification tasks, such as the five-labels classification task used in this task. Conclusion, the sentiment detection task on the YouTube can improve performance by the proposed multi-dimensional sentiment indicators and our solution to modify the structure on classifiers.