Affective computing
Knowledge engineering: principles and methods
Data & Knowledge Engineering - Special jubilee issue: DKE 25
Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
ConceptNet — A Practical Commonsense Reasoning Tool-Kit
BT Technology Journal
Finding parts in very large corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Determining the semantic orientation of terms through gloss classification
Proceedings of the 14th ACM international conference on Information and knowledge management
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 extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Corpus-based semantic role approach in information retrieval
Data & Knowledge Engineering
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
SemEval-2007 task 14: affective text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Developing HEO human emotions ontology
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
IEEE Transactions on Affective Computing
IEEE Transactions on Affective Computing
Affect analysis of text using fuzzy semantic typing
IEEE Transactions on Fuzzy Systems
Inferring the semantic properties of sentences by mining syntactic parse trees
Data & Knowledge Engineering
A new case-based classification using incremental concept lattice knowledge
Data & Knowledge Engineering
Domain taxonomy learning from text: The subsumption method versus hierarchical clustering
Data & Knowledge Engineering
Categorization of malicious behaviors using ontology-based cognitive agents
Data & Knowledge Engineering
Data & Knowledge Engineering
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In the past years, an important volume of research in Natural Language Processing has concentrated on the development of automatic systems to deal with affect in text. The different approaches considered dealt mostly with explicit expressions of emotion, at word level. Nevertheless, expressions of emotion are often implicit, inferrable from situations that have an affective meaning. Dealing with this phenomenon requires automatic systems to have ''knowledge'' on the situation, and the concepts it describes and their interaction, to be able to ''judge'' it, in the same manner as a person would. This necessity motivated us to develop the EmotiNet knowledge base - a resource for the detection of emotion from text based on commonsense knowledge on concepts, their interaction and their affective consequence. In this article, we briefly present the process undergone to build EmotiNet and subsequently propose methods to extend the knowledge it contains. We further on analyse the performance of implicit affect detection using this resource. We compare the results obtained with EmotiNet to the use of alternative methods for affect detection. Following the evaluations, we conclude that the structure and content of EmotiNet are appropriate to address the automatic treatment of implicitly expressed affect, that the knowledge it contains can be easily extended and that overall, methods employing EmotiNet obtain better results than traditional emotion detection approaches.