论文信息 - Hybrid Model for Stress Detection in Social Media by Using Dynamic Factor Graph Model and Convolutional Neural Networks - 字舞流文

Hybrid Model for Stress Detection in Social Media by Using Dynamic Factor Graph Model and Convolutional Neural Networks

L. Ramanathan | R. Kannadasan | N. Prabakaran

[1] Peter Bühlmann. Regression shrinkage and selection via the Lasso: a retrospective (Robert Tibshirani): Comments on the presentation , 2011 .

[2] Ming Zhou,et al. Collective Semantic Role Labeling for Tweets with Clustering , 2011, IJCAI.

[3] Ronald J. Williams,et al. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[4] John Shawe-Taylor,et al. Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.

[5] Ke Xu,et al. MoodLens: an emoticon-based sentiment analysis system for chinese tweets , 2012, KDD.

[6] R. Tibshirani,et al. Regression shrinkage and selection via the lasso: a retrospective , 2011 .

[7] Thomas Bräunl,et al. Performance Comparison of Bug Navigation Algorithms , 2007, J. Intell. Robotic Syst..

[8] Yiran Chen,et al. MoodCast: Emotion Prediction via Dynamic Continuous Factor Graph Model , 2010, 2010 IEEE International Conference on Data Mining.