Learning Subjective Adjectives from Corpora
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A factuality profiler for eventualities in text
A factuality profiler for eventualities in text
Assessing Sentiment of Text by Semantic Dependency and Contextual Valence Analysis
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Topic identification for fine-grained opinion analysis
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Extracting opinions, opinion holders, and topics expressed in online news media text
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Sentence and expression level annotation of opinions in user-generated discourse
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A verb lexicon model for deep sentiment analysis and opinion mining applications
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
Detecting implicit expressions of sentiment in text based on commonsense knowledge
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
A crowdsourcing platform for the construction of accessibility maps
Proceedings of the 10th International Cross-Disciplinary Conference on Web Accessibility
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Current work on sentiment analysis is characterized by approaches with a pragmatic focus, which use shallow techniques in the interest of robustness but often rely on ad-hoc creation of data sets and methods. We argue that progress towards deep analysis depends on a) enriching shallow representations with linguistically motivated, rich information, and b) focussing different branches of research and combining ressources to create synergies with related work in NLP. In the paper, we propose SentiFrameNet, an extension to FrameNet, as a novel representation for sentiment analysis that is tailored to these aims.