The affective reasoner: a process model of emotions in a multi-agent system
The affective reasoner: a process model of emotions in a multi-agent system
WordNet: a lexical database for English
Communications of the ACM
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Learning Subjective Adjectives from Corpora
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Classifying large data sets using SVMs with hierarchical clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An Effective Support Vector Machines (SVMs) Performance Using Hierarchical Clustering
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
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
Learning subjective nouns using extraction pattern bootstrapping
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Extracting semantic orientations of words using spin model
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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
CLaC and CLaC-NB: knowledge-based and corpus-based approaches to sentiment tagging
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Using emoticons to reduce dependency in machine learning techniques for sentiment classification
ACLstudent '05 Proceedings of the ACL Student Research Workshop
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Support vector machine classification based on fuzzy clustering for large data sets
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Sentic Computing: Techniques, Tools, and Applications
Sentic Computing: Techniques, Tools, and Applications
Enriching SenticNet Polarity Scores through Semi-Supervised Fuzzy Clustering
ICDMW '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining Workshops
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We consider the task of semi-supervised classification: extending category labels from a small dataset of labeled examples to a much larger set. We show that, at least on our case study task, unsupervised fuzzy clustering of the unlabeled examples helps in obtaining the hard clusters. Namely, we used the membership values obtained with fuzzy clustering as additional features for hard clustering. We also used these membership values to reduce the confusion set for the hard clustering. As a case study, we use applied the proposed method to the task of constructing a large emotion lexicon by extending the emotion labels from the WordNet Affect lexicon using various features of words. Some of the features were extracted from the emotional statements of the freely available ISEAR dataset; other features were WordNet distance and the similarity measured via the polarity scores in the SenticNet resource. The proposed method classified words by emotion labels with high accuracy.