@inproceedings{ac598c5e3a24487abf0e7a88aafc076d,
title = "Topic detection with danmaku: A time-sync joint NMF approach",
abstract = "Topic detection on web videos can effectively help collecting users{\textquoteright} feedback and emotional tendency. With the features of relatively short, topic alignment and time synchronization, Danmaku comments can significantly extend the applications of topic detection. However, most of the current topic detection approaches fall short of considering the interior relation between adjacent time-steps which ignores the underlying temporal effects. To address this problem, we introduce a Joint Online Nonnegative Matrix Factorization model (JO-NMF) to detect latent topics with automatically exploiting Danmaku comments. Experimental results show great advantages of our proposed model on real-world Danmaku datasets. The results show our model outperforms baselines in topic detection with perplexity and RMSE for the noisy temporal data.",
keywords = "Crowdsourcing, Danmaku, Nonnegative matrix factorization, Topic detection, Web videos",
author = "Qingchun Bai and Qinmin Hu and Faming Fang and Liang He",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 29th International Conference on Database and Expert Systems Applications, DEXA 2018 ; Conference date: 03-09-2018 Through 06-09-2018",
year = "2018",
doi = "10.1007/978-3-319-98812-2\_39",
language = "英语",
isbn = "9783319988115",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "428--435",
editor = "G{\"u}nther Pernul and Sven Hartmann and Hui Ma and Abdelkader Hameurlain and Wagner, \{Roland R.\}",
booktitle = "Database and Expert Systems Applications - 29th International Conference, DEXA 2018, Proceedings",
address = "德国",
}