Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
The Journal of Machine Learning Research
Sentiment analysis: capturing favorability using natural language processing
Proceedings of the 2nd international conference on Knowledge capture
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Learning subjective nouns using extraction pattern bootstrapping
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Pachinko allocation: DAG-structured mixture models of topic correlations
ICML '06 Proceedings of the 23rd international conference on Machine learning
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Mixtures of hierarchical topics with Pachinko allocation
Proceedings of the 24th international conference on Machine learning
Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
Mining multi-faceted overviews of arbitrary topics in a text collection
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A Joint Topic and Perspective Model for Ideological Discourse
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Evaluation methods for topic models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Predicting response to political blog posts with topic models
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Cross-cultural analysis of blogs and forums with mixed-collection topic models
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Bayesian checking for topic models
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Proceedings of the Workshop on Semantic Analysis in Social Media
Hedge detection as a lens on framing in the GMO debates: a position paper
ExProM '12 Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics
One theme in all views: modeling consensus topics in multiple contexts
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable dynamic nonparametric Bayesian models of content and users
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Keyword extraction for blogs based on content richness
Journal of Information Science
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With the proliferation of user-generated articles over the web, it becomes imperative to develop automated methods that are aware of the ideological-bias implicit in a document collection. While there exist methods that can classify the ideological bias of a given document, little has been done toward understanding the nature of this bias on a topical-level. In this paper we address the problem of modeling ideological perspective on a topical level using a factored topic model. We develop efficient inference algorithms using Collapsed Gibbs sampling for posterior inference, and give various evaluations and illustrations of the utility of our model on various document collections with promising results. Finally we give a Metropolis-Hasting inference algorithm for a semi-supervised extension with decent results.