  Topic modelling is a text mining technique for identifying salient themes
from a number of documents. The output is commonly a set of topics consisting
of isolated tokens that often co-occur in such documents. Manual effort is
often associated with interpreting a topic's description from such tokens.
However, from a human's perspective, such outputs may not adequately provide
enough information to infer the meaning of the topics; thus, their
interpretability is often inaccurately understood. Although several studies
have attempted to automatically extend topic descriptions as a means of
enhancing the interpretation of topic models, they rely on external language
sources that may become unavailable, must be kept up-to-date to generate
relevant results, and present privacy issues when training on or processing
data. This paper presents a novel approach towards extending the output of
traditional topic modelling methods beyond a list of isolated tokens. This
approach removes the dependence on external sources by using the textual data
itself by extracting high-scoring keywords and mapping them to the topic
model's token outputs. To measure the interpretability of the proposed outputs
against those of the traditional topic modelling approach, independent
annotators manually scored each output based on their quality and usefulness,
as well as the efficiency of the annotation task. The proposed approach
demonstrated higher quality and usefulness, as well as higher efficiency in the
annotation task, in comparison to the outputs of a traditional topic modelling
method, demonstrating an increase in their interpretability.
