NLTK: the Natural Language Toolkit
ETMTNLP '02 Proceedings of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics - Volume 1
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
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
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Inter-coder agreement for computational linguistics
Computational Linguistics
Natural Language Processing with Python
Natural Language Processing with Python
Emotion Recognition in Text for 3-D Facial Expression Rendering
IEEE Transactions on Multimedia
Interactive data-driven discovery of temporal behavior models from events in media streams
Proceedings of the 20th ACM international conference on Multimedia
Interactive data-driven search and discovery of temporal behavior patterns from media streams
Proceedings of the 20th ACM international conference on Multimedia
Actor level emotion magnitude prediction in text and speech
Multimedia Tools and Applications
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Improvement in human computer interaction requires effective and rapid development of multimedia systems that can understand and interact with humans. These systems need resources to train and learn how to interpret human emotions. Currently, there is a relative small number of existing resources such as annotated corpora that can be used for affect and multimodal content detection. In this paper, an extension of an existing corpus is presented. The corpus includes new annotations for affect magnitude detection and anaphora resolution. The format of the collected data is presented, along with the annotation methodology, basic statistics, suggestions for possible uses, and future work. This corpus is an extension of the UIUC Affect corpus of children's stories. The corpus includes new automatic annotations using Natural Language Processing toolkits as well as new manual annotations for affect magnitude detection and anaphora resolution. Results of inter-annotator agreement analysis on a subset of the corpus are also presented.