Ching-Wen Hsu


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A Pretrained YouTuber Embeddings for Improving Sentiment Classification of YouTube Comments
Ching-Wen Hsu | Hsuan Liu | Jheng-Long Wu
International Journal of Computational Linguistics & Chinese Language Processing, Volume 26, Number 2, December 2021

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A Corpus for Dimensional Sentiment Classification on YouTube Streaming Service
Ching-Wen Hsu | Chun-Lin Chou | Hsuan Liu | Jheng-Long Wu
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

The streaming service platform such as YouTube provides a discussion function for audiences worldwide to share comments. YouTubers who upload videos to the YouTube platform want to track the performance of these uploaded videos. However, the present analysis functions of YouTube only provide a few performance indicators such as average view duration, browsing history, variance in audience’s demographics, etc., and lack of sentiment analysis on the audience’s comments. Therefore, the paper proposes multi-dimensional sentiment indicators such as YouTuber preference, Video preferences, and Excitement level to capture comprehensive sentiment on audience comments for videos and YouTubers. To evaluate the performance of different classifiers, we experiment with deep learning-based, machine learning-based, and BERT-based classifiers to automatically detect three sentiment indicators of an audience’s comments. Experimental results indicate that the BERT-based classifier is a better classification model than other classifiers according to F1-score, and the sentiment indicator of Excitement level is quite an improvement. Therefore, the multiple sentiment detection tasks on the video streaming service platform can be solved by the proposed multi-dimensional sentiment indicators accompanied with BERT classifier to gain the best result.