Direction-based text interpretation as an information access refinement
Text-based intelligent systems
A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
Tracking point of view in narrative
Computational Linguistics
Identifying subjective characters in narrative
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 2
Emotions from text: machine learning for text-based emotion prediction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Exploiting subjectivity classification to improve information extraction
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Semi-supervised named entity recognition: learning to recognize 100 entity types with little supervision
Affect analysis of text using fuzzy semantic typing
IEEE Transactions on Fuzzy Systems
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This paper describes the preliminary results of a system for extracting sentiments opinioned with regard with named entities. It also combines rule-based classification, statistics and machine learning in a new method. The accuracy and speed of extraction and classification are crucial. The service oriented architecture permits the end-user to work with a flexible interface in order to produce applications that range from aggregating consumer feedback on commercial products to measuring public opinion on political issues from blog and forums. The experiment has two versions available for testing, one with concrete extraction results and sentiment calculus and the other with internal metrics validation results.