At the LSE Computational Social Science hackathon, I presented how to develop text analysis models using quanteda‘s core API’s such as as.tokens(), as.dfm() and pattern2id(). All the slides and the files available are in my Github repository.
Analyze big data with small RAM
A lot of people are using quanteda to analyze social media posts because it is very fast and flexible, but they sometimes face dramatic slow down due to memory swapping caused by insufficient sizes of RAM. quanteda requires the size of RAM to be 5 times larger than the data to analyze, but it can […]
Relaxing R version requirement
Until quanteda v1.1, our users needed to have R 3.4.0 installed, but we relax the requirement to R 3.1.0, because people working in companies or other large organizations often do not have latest version of R in their computers, and therefore cannot use our package. To investigate why quanteda requires R 3.4.0 quickly, I wrote […]