  Whether it is in the form of transcribed conversations, blog posts, or
tweets, qualitative data provides a reader with rich insight into both the
overarching trends as well as the diversity of human ideas expressed through
text. Handling and analyzing large amounts of qualitative data, however, is
difficult, often requiring multiple time-intensive perusals in order to
identify patterns. This difficulty is multiplied with each additional question
or time point present in a data set. A primary challenge then is creating
visualizations that support the interpretation of qualitative data by making it
easier to identify and explore trends of interest. By combining the affordances
of both text and visualizations, WordStream has previously enabled ease of
information retrieval and processing of time-series text data, but the
data-wrangling necessary to produce a WordStream remains a significant barrier
for non-technical users. In response, this paper presents WordStream Maker: an
end-to-end platform with a pipeline that utilizes natural language processing
(NLP) to help non-technical users process raw text data and generate a
customizable visualization without programming practice. Lessons learned from
integrating NLP into visualization and scaling to large data sets are
discussed, along with use cases to demonstrate the usefulness of the platform.
