A simple rule-based part of speech tagger
HLT '91 Proceedings of the workshop on Speech and Natural Language
Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Feature subsumption for opinion analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Multimodal subjectivity analysis of multiparty conversation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Summarizing spoken and written conversations
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
The AMI meeting corpus: a pre-announcement
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Latent mixture of discriminative experts for multimodal prediction modeling
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
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In this research we aim to detect subjective sentences in multimodal conversations. We introduce a novel technique wherein subjective patterns are learned from both labeled and unlabeled data, using n-gram word sequences with varying levels of lexical instantiation. Applying this technique to meeting speech and email conversations, we gain significant improvement over state-of-the-art approaches. Furthermore, we show that coupling the pattern-based approach with features that capture characteristics of general conversation structure yields additional improvement.