Knowledge engineering: principles and methods
Data & Knowledge Engineering - Special jubilee issue: DKE 25
A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
ConceptNet — A Practical Commonsense Reasoning Tool-Kit
BT Technology Journal
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 subjective nouns using extraction pattern bootstrapping
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
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
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
SemEval-2007 task 14: affective text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
IEEE Transactions on Affective Computing
Affect analysis of text using fuzzy semantic typing
IEEE Transactions on Fuzzy Systems
Decision Support Systems
Semantic frames as an anchor representation for sentiment analysis
WASSA '12 Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis
Enhancing sentiment extraction from text by means of arguments
Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining
Hi-index | 0.00 |
Sentiment analysis is one of the recent, highly dynamic fields in Natural Language Processing. Most existing approaches are based on word-level analysis of texts and are able to detect only explicit expressions of sentiment. In this paper, we present an approach towards automatically detecting emotions (as underlying components of sentiment) from contexts in which no clues of sentiment appear, based on commonsense knowledge. The resource we built towards this aim -- EmotiNet - is a knowledge base of concepts with associated affective value. Preliminary evaluations show that this approach is appropriate for the task of implicit emotion detection, thus improving the performance of sentiment detection and classification in text.