LaMa: a thematic labelling web application

Qualitative analysis of data is relevant for a variety of domains including empirical research studies and social sciences. While performing qualitative analysis of large textual data sets such as data from interviews, surveys, mailing lists, and code repositories, condensing pieces of data into a set of terms or keywords simplifies analysis, and helps in obtaining useful insight. This condensation of data can be achieved by associating keywords, a.k.a. labels, with text fragments, a.k.a artifacts. It is essential during this type of research to achieve greater accuracy, facilitate collaboration, build consensus, and limit bias. LaMa, short for Labelling Machine, is an open source web application developed for aiding in thematic analysis of qualitative data. The source code and the documentation of the tool are available at https://github.com/muctadir/lama. In addition to being open-source, LaMa facilitates thematic analysis through features such as artifact based collaborative labelling, consensus building through conflict resolution techniques, grouping of labels into themes, and private installation with complete control over research data. With the help of this tool and flow it enforces, thematic analysis becomes less time consuming and more structured.