BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Tracking point of view in narrative
Computational Linguistics
Recognizing subjectivity: a case study in manual tagging
Natural Language Engineering
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Development and use of a gold-standard data set for subjectivity classifications
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on 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
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 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
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
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Identifying sources of opinions with conditional random fields and extraction patterns
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Detecting emotion in speech: experiments in three domains
NAACL-DocConsortium '06 Proceedings of the 2006 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume: doctoral consortium
Spoken Versus Typed Human and Computer Dialogue Tutoring
International Journal of Artificial Intelligence in Education
Just how mad are you? finding strong and weak opinion clauses
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Extracting opinions, opinion holders, and topics expressed in online news media text
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Toward opinion summarization: linking the sources
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Sequential Decision Strategies for Machine Interpretation of Speech
IEEE Transactions on Audio, Speech, and Language Processing
Computer Speech and Language
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This paper describes a system for automatic opinion analysis from spoken messages collected in the context of a user satisfaction survey. Opinion analysis is performed from the perspective of opinion monitoring. A process is outlined for detecting segments expressing opinions in a speech signal. Methods are proposed for accepting or rejecting segments from messages that are not reliably analyzed due to the limitations of automatic speech recognition processes, for assigning opinion hypotheses to segments and for evaluating hypothesis opinion proportions. Specific language models are introduced for representing opinion concepts. These models are used for hypothesizing opinion carrying segments in a spoken message. Each segment is interpreted by a classifier based on the Adaboost algorithm which associates a pair of topic and polarity labels to each segment. The different processes are trained and evaluated on a telephone corpus collected in a deployed customer care service. The use of conditional random fields (CRFs) is also considered for detecting segments and results are compared for different types of data and approaches. By optimizing the choice of the strategy parameters, it is possible to estimate user opinion proportions with a Kullback-Leibler divergence of 0.047 bits with respect to the true proportions obtained with a manual annotation of the spoken messages. The proportions estimated with such a low divergence are accurate enough for monitoring user satisfaction over time.