Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for 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
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Automatic identification of pro and con reasons in online reviews
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Flink: Semantic Web technology for the extraction and analysis of social networks
Web Semantics: Science, Services and Agents on the World Wide Web
Extracting social networks from literary fiction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Decision Support Systems
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To assist in the research of social networks in history, we develop machine-learning-based tools for the identification and classification of personal relationships. Our case study focuses on the Dutch social movement between 1870 and 1940, and is based on biographical texts describing the lives of notable people in this movement. We treat the identification and the labeling of relations between two persons into positive, neutral, and negative both as a sequence of two tasks and as a single task. We observe that our machine-learning classifiers, support vector machines, produce better generalization performance on the single task. We show how a complete social network can be built from these classifications, and provide a qualitative analysis of the induced network using expert judgements on samples of the network.