Knowledge lean word-sense disambiguation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
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
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Unsupervised Discrimination of Person Names in Web Contexts
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
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
Learning with compositional semantics as structural inference for subsentential sentiment analysis
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Offensive language detection using multi-level classification
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
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We describe a project undertaken by an interdisciplinary team of researchers in sleep and in and machine learning. The goal is sentiment extraction from a corpus containing short textual descriptions of dreams. Dreams are categorized in a four-level scale of affections. The approach is based on a novel representation, taking into account the leading themes of the dream and the sequential unfolding of associated affective feelings during the dream. The dream representation is based on three combined parts, two of which are automatically produced from the description of the dream. The first part consists of co-occurrence vectors, which ---unlike the standard Bag-of-words model ---capture non-local relationships between meanings of word in a corpus. The second part introduces the dynamic representation that captures the change in affections throughout the progress of the dream. The third part is the self-reported assessment of the dream by the dreamer according to eight given attributes. The three representations are subject to aggressive feature selection. Using an ensemble of classifiers and the combined 3-partite representation, we have achieved 64% accuracy, which is in the range of human experts' consensus in that domain.