Open hope-data-science opened 4 years ago
Response:
Try :
library(akc)
bibli_data_table %>%
keyword_clean(id = "id",keyword = "keyword") %>%
keyword_group(id = "id",keyword = "keyword") %>%
keyword_network() + ggplot2::scale_fill_viridis_d()
I believe you can add more colors with "scalefill" from ggplot2
. So I don't use it directly here.
Since now you can get the raw table from the results, just try ggwordcloud
and DIY. akc
just provides a robust method to get a good-enough-for-publish result. From that package, you can absolutely get more options to adjust. (Still, I will add it when I am free)
This is the scope for larger than version 1.0.0. akc
now focuses on grouping based on community detection (as you could find in the logo). Vectorizing would be done outside of akc
, but akc
should be able to receive word vectors and cluster them automatically. That is in the plan.
What is the theory (paper) behind automatic clustering?
Community detection in network science.