Abstract
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.
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011) |
| Publisher | Association for Computational Linguistics |
| Pages | 61-69 |
| Publication status | Published - 2011 |
| Externally published | Yes |