The Journal of Machine Learning Research
Emotions from text: machine learning for text-based emotion prediction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Learning to identify emotions in text
Proceedings of the 2008 ACM symposium on Applied computing
Learning from labeled features using generalized expectation criteria
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Emotion Classification of Online News Articles from the Reader's Perspective
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
MedLDA: maximum margin supervised topic models for regression and classification
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Joint sentiment/topic model for sentiment analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Posterior Regularization for Structured Latent Variable Models
The Journal of Machine Learning Research
Partially labeled topic models for interpretable text mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised recursive autoencoders for predicting sentiment distributions
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Weakly Supervised Joint Sentiment-Topic Detection from Text
IEEE Transactions on Knowledge and Data Engineering
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Sentiment analysis has long focused on binary classification of text as either positive or negative. There has been few work on mapping sentiments or emotions into multiple dimensions. This paper studies a Bayesian modeling approach to multi-class sentiment classification and multidimensional sentiment distributions prediction. It proposes effective mechanisms to incorporate supervised information such as labeled feature constraints and document-level sentiment distributions derived from the training data into model learning. We have evaluated our approach on the datasets collected from the confession section of the Experience Project website where people share their life experiences and personal stories. Our results show that using the latent representation of the training documents derived from our approach as features to build a maximum entropy classifier outperforms other approaches on multi-class sentiment classification. In the more difficult task of multi-dimensional sentiment distributions prediction, our approach gives superior performance compared to a few competitive baselines.