C4.5: programs for machine learning
C4.5: programs for machine learning
Making large-scale support vector machine learning practical
Advances in kernel methods
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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
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
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Improve the effectiveness of the opinion retrieval and opinion polarity classification
Proceedings of the 17th ACM conference on Information and knowledge management
Learning with compositional semantics as structural inference for subsentential sentiment analysis
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
SemEval-2007 task 14: affective text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Dependency tree-based sentiment classification using CRFs with hidden variables
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A survey on the role of negation in sentiment analysis
NeSp-NLP '10 Proceedings of the Workshop on Negation and Speculation in Natural Language Processing
A hybrid approach to emotional sentence polarity and intensity classification
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
The impact of valence shifters on mining implicit economic opinions
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
Topic sentiment change analysis
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
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 generate-and-test method of detecting negative-sentiment sentences
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Modality and negation: An introduction to the special issue
Computational Linguistics
Information Retrieval on the Blogosphere
Foundations and Trends in Information Retrieval
A lexicon model for deep sentiment analysis and opinion mining applications
Decision Support Systems
UMichigan: a conditional random field model 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
FBK: exploiting phrasal and contextual clues for negation scope detection
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
A generalised hybrid architecture for NLP
HYBRID '12 Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data
Ensemble learning for sentiment classification
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
Bootstrapping polarity classifiers with rule-based classification
Language Resources and Evaluation
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We investigate the problem of determining the polarity of sentiments when one or more occurrences of a negation term such as "not" appear in a sentence. The concept of the scope of a negation term is introduced. By using a parse tree and typed dependencies generated by a parser and special rules proposed by us, we provide a procedure to identify the scope of each negation term. Experimental results show that the identification of the scope of negation improves both the accuracy of sentiment analysis and the retrieval effectiveness of opinion retrieval.