Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Development and use of a gold-standard data set for subjectivity classifications
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Using appraisal groups for sentiment analysis
Proceedings of the 14th ACM international conference on Information and knowledge management
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
SenseRelate::TargetWord: a generalized framework for word sense disambiguation
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
A metalearning approach to processing the scope of negation
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
SWAT-MP: the SemEval-2007 systems for task 5 and task 14
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UPAR7: a knowledge-based system for headline sentiment tagging
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
The effect of negation on sentiment analysis and retrieval effectiveness
Proceedings of the 18th ACM conference on Information and knowledge management
Sentence level sentiment analysis in the presence of conjuncts using linguistic analysis
ECIR'07 Proceedings of the 29th European conference on IR research
A joint model of feature mining and sentiment analysis for product review rating
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Improving document-level sentiment classification using contextual valence shifters
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
UCM-I: a rule-based syntactic approach for resolving the scope of negation
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
An enhanced semantic tree kernel for sentiment polarity classification
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
Factored semantic sequence kernel for sentiment polarity classification
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
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In this paper, the authors present a new approach to sentence level sentiment analysis. The aim is to determine whether a sentence expresses a positive, negative or neutral sentiment, as well as its intensity. The method performs WSD over the words in the sentence in order to work with concepts rather than terms, and makes use of the knowledge in an affective lexicon to label these concepts with emotional categories. It also deals with the effect of negations and quantifiers on polarity and intensity analysis. An extensive evaluation in two different domains is performed in order to determine how the method behaves in 2-classes (positive and negative), 3-classes (positive, negative and neutral) and 5-classes (strongly negative, weakly negative, neutral, weakly positive and strongly positive) classification tasks. The results obtained compare favorably with those achieved by other systems addressing similar evaluations.