The nature of statistical learning theory
The nature of statistical learning theory
Extraction and classification of facemarks
Proceedings of the 10th international conference on Intelligent user interfaces
Emoticons and Online Message Interpretation
Social Science Computer Review
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Data quality from crowdsourcing: a study of annotation selection criteria
HLT '09 Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing
Using emoticons to reduce dependency in machine learning techniques for sentiment classification
ACLstudent '05 Proceedings of the ACL Student Research Workshop
Distant supervision for relation extraction without labeled data
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
A latent variable model for geographic lexical variation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Creating subjective and objective sentence classifiers from unannotated texts
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Distant supervision for emotion classification with discrete binary values
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
Joint learning on sentiment and emotion classification
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A survey of noise reduction methods for distant supervision
Proceedings of the 2013 workshop on Automated knowledge base construction
| Hi-index | 0.00 |
We describe a set of experiments using automatically labelled data to train supervised classifiers for multi-class emotion detection in Twitter messages with no manual intervention. By cross-validating between models trained on different labellings for the same six basic emotion classes, and testing on manually labelled data, we conclude that the method is suitable for some emotions (happiness, sadness and anger) but less able to distinguish others; and that different labelling conventions are more suitable for some emotions than others.