The socialist network

We develop and test machine learning‐based tools for the classification of personal relationships in biographical texts, and the induction of social networks from these classifications. A case study is presented based on several hundreds of biographies of notable persons in the Dutch social movement. Our classifiers mark relations between two persons (one being the topic of a biography, the other being mentioned in this biography) as positive, neutral, or unknown, and do so at an above‐baseline level. A training set centering on a historically important person is contrasted against a multi‐person training set; the latter is found to produce the most robust generalization performance. Frequency‐ranked predictions of positive and negative relationships predicted by the best‐performing classifier, presented in the form of person‐centered social networks, are scored by a domain expert; the mean average precision results indicate that our system is better in classifying and ranking positive relations (around 70% MAP) than negative relations (around 40% MAP).