Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
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
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
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
Document-Word Co-regularization for Semi-supervised Sentiment Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Sentiment analysis of blogs by combining lexical knowledge with text classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Expanding domain sentiment lexicon through double propagation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Leveraging relationships in social networks for sentiment analysis
Proceedings of the 18th Brazilian symposium on Multimedia and the web
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Multi grain sentiment analysis is the task of simultaneously classifying sentiment expressed at different levels of granularity, as opposed to single level at a time. Models built for multi grain sentiment analysis assume fully labeled corpus at fine grained level or coarse grained level or both. Huge amount of online reviews are not fully labeled at any of the levels, but are partially labeled at both the levels. We propose a multi grain collective classification framework to not only exploit the information available at all the levels but also use intra dependencies at each level and inter dependencies between the levels. We demonstrate empirically that the proposed framework enables better performance at both the levels compared to baseline approaches.