Abstract
Natural language resources are essential for integrating linguistic engineering components into information processing suites. However, the resources available in French are scarce and do not cover all possible tasks, especially for specific business applications. In this context, we present a dataset of French newsletters and their use to predict their impact, good or bad, on readers. We propose an original representation of newsletters in the form of graphs that take into account the layout of the newsletters. We then evaluate the interest of such a representation in predicting a newsletter’s performance in terms of open and click rates using graph convolution network models.
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Notes
- 1.
Kosmopolead is a UNEEK’s trademark offering services such as CRM (https://www.kosmopolead.com/).
- 2.
With K set to 5, which here represents the number of colors to detect. It is rare to find more than 5 colors in the same portion of the image, and if it is the case, we only focus here on the dominant color.
- 3.
As defined in [25].
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Blandin, A., Saïd, F., Villaneau, J., Marteau, PF. (2022). DaFNeGE: Dataset of French Newsletters with Graph Representation and Embedding. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2022. Lecture Notes in Computer Science(), vol 13502. Springer, Cham. https://doi.org/10.1007/978-3-031-16270-1_2
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