Abstract
Ensuring viewpoint diversity in mass media is a historical challenge and recent political events, and the ever-increased use of the Internet, have made it an increasingly critical and contentious issue. This research explores the relationship between semantic structures and viewpoint; demonstrating that the viewpoint diversity in a selection of documents can be increased by utilizing extracted semantic and sentiment features. Small portions of documents matching search terms were embedded in a semantic space using word vectors and sentiment scores. The resulting features were used to train a support vector machine to differentiate documents by viewpoint in a topically homogeneous corpus. When evaluating the top 10% most probable predictions for each viewpoint, this approach yielded a lift of between 1.26 and 2.04.
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Notes
- 1.
Source code is available at https://github.com/jeffharwell/viewpointdiversity.
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Harwell, J., Li, Y. (2022). Classifying Documents by Viewpoint Using Word2Vec and Support Vector Machines. In: Rosso, P., Basile, V., Martínez, R., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2022. Lecture Notes in Computer Science, vol 13286. Springer, Cham. https://doi.org/10.1007/978-3-031-08473-7_13
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