Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Bipartite graph partitioning and data clustering
Proceedings of the tenth international conference on Information and knowledge management
Incorporating Prior Knowledge into Boosting
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Incorporating prior knowledge with weighted margin support vector machines
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Question Answering via Bayesian inference on lexical relations
MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Movie review mining and summarization
CIKM '06 Proceedings of the 15th 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
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge transformation from word space to document space
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning from labeled features using generalized expectation criteria
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Document-Word Co-regularization for Semi-supervised Sentiment Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Text classification by labeling words
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
TextGraphs-1 Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing
Cross-domain sentiment classification via spectral feature alignment
Proceedings of the 19th international conference on World wide web
A comparative study of Bayesian models for unsupervised sentiment detection
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Learning sentiment classification model from labeled features
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Active deep networks for semi-supervised sentiment classification
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Which clustering do you want? inducing your ideal clustering with minimal feedback
Journal of Artificial Intelligence Research
Self-training from labeled features for sentiment analysis
Information Processing and Management: an International Journal
Aspect ranking: identifying important product aspects from online consumer reviews
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Cross-lingual sentiment classification via bi-view non-negative matrix tri-factorization
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Collaborative data cleaning for sentiment classification with noisy training corpus
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Bilingual co-training for sentiment classification of chinese product reviews
Computational Linguistics
Orthogonal nonnegative matrix tri-factorization for semi-supervised document co-clustering
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Supervised input space scaling for non-negative matrix factorization
Signal Processing
Incorporating Sentiment Prior Knowledge for Weakly Supervised Sentiment Analysis
ACM Transactions on Asian Language Information Processing (TALIP)
Using key sentence to improve sentiment classification
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Semi-supervised learning for imbalanced sentiment classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
SumView: A Web-based engine for summarizing product reviews and customer opinions
Expert Systems with Applications: An International Journal
Cross-lingual mixture model for sentiment classification
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Dual word and document seed selection for semi-supervised sentiment classification
Proceedings of the 21st ACM international conference on Information and knowledge management
WikiSent: weakly supervised sentiment analysis through extractive summarization with wikipedia
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Exploring weakly supervised latent sentiment explanations for aspect-level review analysis
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Listening to the crowd: automated analysis of events via aggregated twitter sentiment
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
A weakly supervised approach to Chinese sentiment classification using partitioned self-training
Journal of Information Science
Discriminative Orthogonal Nonnegative matrix factorization with flexibility for data representation
Expert Systems with Applications: An International Journal
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Sentiment classification refers to the task of automatically identifying whether a given piece of text expresses positive or negative opinion towards a subject at hand. The proliferation of user-generated web content such as blogs, discussion forums and online review sites has made it possible to perform large-scale mining of public opinion. Sentiment modeling is thus becoming a critical component of market intelligence and social media technologies that aim to tap into the collective wisdom of crowds. In this paper, we consider the problem of learning high-quality sentiment models with minimal manual supervision. We propose a novel approach to learn from lexical prior knowledge in the form of domain-independent sentiment-laden terms, in conjunction with domain-dependent unlabeled data and a few labeled documents. Our model is based on a constrained non-negative tri-factorization of the term-document matrix which can be implemented using simple update rules. Extensive experimental studies demonstrate the effectiveness of our approach on a variety of real-world sentiment prediction tasks.