WordNet: a lexical database for English
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ACL '02 Proceedings of the 40th Annual Meeting on Association for 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
Hierarchical directed acyclic graph kernel: methods for structured natural language data
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
NAACL-Demonstrations '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Demonstration Session
SemEval-2007 task 14: affective text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Using measures of semantic relatedness for word sense disambiguation
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Convolution kernels for opinion holder extraction
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A hybrid approach to emotional sentence polarity and intensity classification
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Discovering fine-grained sentiment with latent variable structured prediction models
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Improving the impact of subjectivity word sense disambiguation on contextual opinion analysis
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Robust sense-based sentiment classification
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
Harnessing WordNet senses for supervised sentiment classification
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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Sentiment analysis has gained a lot of attention in recent years, mainly due to the many practical applications it supports and a growing demand for such applications. This growing demand is supported by an increasing amount and availability of opinionated online information, mainly due to the proliferation and popularity of social media. The majority of work in sentiment analysis considers the polarity of word terms rather than the polarity of specific senses of the word in context. However there has been an increased effort in distinguishing between different senses of a word as well as their different opinion-related properties. Syntactic parse trees are a widely used natural language processing construct that has been effectively employed for text classification tasks. This paper proposes a novel methodology for extending syntactic parse trees, based on word sense disambiguation and context specific opinion-related features. We evaluate the methodology on three publicly available corpuses, by employing the sub-set tree kernel as a similarity function in a support vector machine. We also evaluate the effectiveness of several publicly available sense specific sentiment lexicons. Experimental results show that all our extended parse tree representations surpass the baseline performance for every measure and across all corpuses, and compared well to other state-of-the-art techniques.