Semantic representations of near-synonyms for automatic lexical choice
Semantic representations of near-synonyms for automatic lexical choice
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
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
From words to senses: a case study of subjectivity recognition
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Subjectivity recognition on word senses via semi-supervised mincuts
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter 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
A lexicon model for deep sentiment analysis and opinion mining applications
Decision Support Systems
Sense-level subjectivity in a multilingual setting
Computer Speech and Language
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There has been extensive work on eliciting human judgements on the sentiment of words and the resulting annotated word lists have frequently been used for opinion mining applications in Natural Language Processing (NLP). However, this word-based approach does not take different senses of a word into account, which might differ in whether and what kind of sentiment they evoke. In this paper, we therefore introduce a human annotation scheme for judging both the subjectivity and polarity of word senses. We show that the scheme is overall reliable, making this a well-defined task for automatic processing. We also discuss three issues that surfaced during annotation: the role of annotation bias, hierarchical annotation (or underspecification) and bias in the sense inventory used.