quanteda has become my first stop for content analysis, for both dictionary- and supervised-learning-focused projects. Because it draws together so many different techniques, it facilitates a more comprehensive analysis of text as well – it’s now very straightforward to compare results from several different approaches. Both my research and teaching on content analysis are increasingly based on quanteda.

The quanteda package has become the starting point for all my text analysis projects in R. This is due to its superb ability to prepare the data for analysis regardless of the method one might prefer. When the data is prepared, quanteda’s convert function makes it easy to transfer the data to a wide range of other packages such as topicmodels or lsa.

quanteda is an excellent resource for both research and teaching than complements R in a way that is invaluable to me — only switching to Python would offer comparable benefits. It is far superior to related packages (e.g. tm) and so well documented that I use it centrally when teaching text mining.

quanteda has been a greatly valuable tool for my research. Text analysis is a rapidly expanding field with new applications appearing rapidly in the social sciences. This package simplifies many of the tasks and allows me to implement more sophisticated classifiers for a more detailed analysis.

I recommend quanteda to students in the Johns Hopkins University Data Science Specialization Capstone project. quanteda provides a rich set of text analysis features coupled with excellent performance relative to Java-based R packages for text analysis.