Foundations of statistical natural language processing
Foundations of statistical natural language processing
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Ontology-Driven Affective Chinese Text Analysis and Evaluation Method
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Research on Focal Figures-Oriented Sentimental Question Answering
ALPIT '08 Proceedings of the 2008 International Conference on Advanced Language Processing and Web Information Technology
Building emotion lexicon from weblog corpora
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Construction of a blog emotion corpus for Chinese emotional expression analysis
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Improving gender classification of blog authors
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
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Text affective computing or sentiment analysis is an important research domain for natural language process, and it requires a large-scale emotion corpus, which can significantly support emotion classification and recognition. In this paper, some experiences and basic rules about constructing a Chinese emotional corpus are discussed, which include data collecting coverage, annotation system and quality criterion. Now there are 163,813 sentences labeled in our Chinese emotional corpus, and works like automatic emotion ontology learning, emotion transformation, gender analysis and emotion schema mining are available based on its statistic and labeled data. This effectively proves the value of our Chinese emotional corpus.