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The socialist network
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AI
DOI：10.1016/j.dss.2012.05.031.png)
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Abstract
En 中文
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). (C) 2012 Elsevier B.V. All rights reserved.
Keywords:
Text mining
Machine learning
Social network extraction
Sentiment analysis
Social history