Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Graph based multi-modality learning
Proceedings of the 13th annual ACM international conference on Multimedia
Spectral clustering and transductive learning with multiple views
Proceedings of the 24th international conference on Machine learning
Semi-supervised polarity lexicon induction
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
TextGraphs-1 Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing
Graph-based multi-modality learning for topic-focused multi-document summarization
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A cognitive-based annotation system for emotion computing
ACL-IJCNLP '09 Proceedings of the Third Linguistic Annotation Workshop
Chinese emotion lexicon developing via multi-lingual lexical resources integration
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
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For sentiment analysis, lexicons play an important role in many related tasks. In this paper, aiming to build Chinese emotion lexicons for public use, we adopted a graph-based algorithm which ranks words according to a few seed emotion words. The ranking algorithm exploits the similarity between words, and uses multiple similarity metrics which can be derived from dictionaries, unlabeled corpora or heuristic rules. To evaluate the adopted algorithm and resources, two independent judges were asked to label the top words of ranking list. It is observed that noise is almost unavoidable due to imprecise similarity metrics between words. So, to guarantee the quality of emotion lexicons, we use an iterative feedback to combine manual labeling and the automatic ranking algorithm above. We also compared our newly constructed Chinese emotion lexicons (happiness, anger, sadness, fear and surprise) with existing counterparts, and related analysis is offered.