Building narratives using Word Clouds and Visual Trees

Businesses leverage data visualization like charts and graphs in the Quantitative Research domain; but what about unstructured text data? Enter Word Clouds and Visual Trees.

Word Clouds are a visually striking method to highlight key textual data points. Visual Trees help deep-diving into the meaning and context of specific words. Think of it like a tree with a main idea (trunk) leading to different aspects of that idea (branches) appearing in the data.

In the realm of Qualitative Research reporting, it's crucial to acknowledge that word clouds, while not a standalone analytical tool, can play a pivotal role in shaping the narrative. The key lies in understanding how to harness them effectively.

Here's how these easy to access tools can help you build your insights narrative:

1. Create word cloud

There are several tools you can use to create a Word Cloud, including free cloud generator software. Try Voyant tools for running analysis without registering or give a shot to free word cloud generator or try your transcription through myTranscriptionplace. 

2. Identifying Meaningful and Unique Terms

Word Clouds excel at spotlighting frequently used terms, helping researchers identify those that are truly meaningful or unique to the study. This curation process allows researchers to filter out words that might add little analytical value. The result is a condensed set of terms that form the foundation for a more focused and purposeful analysis.

3. Contextual Understanding with Visual Trees:

Given the inherent lack of context in Word Clouds, researchers have the option to transition to complementary tools such as Visual Trees. For each meaningful word extracted from the Word Cloud, avVisual Tree can provide deeper understanding of the context in which these words were used. This step is crucial, as it allows researchers to explore nuances and intricacies surrounding the identified terms.

4. Building Hypotheses and Weaving the Story

Let's demonstrate this with an example:

Step 1: Generating word cloud

Here is the Word Cloud for a YouTube video on Sustainability that we ended up transcribing:

Step 2: Identifying the meaningful terms:

From the above example, it's clear that the word "think" is repeated many times because that is the way of expression for the Speaker.  But the relevant keywords are "sustainability", "energy", "carbon" and "renewable". 

Step 3:

And here is the visual tree we ended up creating; for the words "energy", "sustainability" and "carbon":

Sustainability 

Carbon

Step 4:

Once we dive deeper into these Visual Trees, it's easier to figure out what is happening in the discussion and weave a narrative. For instance, it looks like: 

A. There are discussions about defining sustainability and how it overlaps with ESG. 

B. There is a discussion around renewable energy, its storage and consumption.

C. A key theme is - getting net carbon zero and neutrality status. 

With these assumptions, we can get a fair idea on what to look for in a transcript, before reading it. 

What does this mean, for Qualitative data analysis? 

In essence, while Word Clouds may not offer the depth of analysis required for a comprehensive understanding, they function as a catalyst for efficiently kickstarting the report-writing process. By leveraging Word Clouds alongside complementary tools, researchers can swiftly identify key themes, sift through meaningful terms, delve into contextual nuances, and ultimately construct a narrative that reflects the rich tapestry of qualitative insights. In the researcher's toolkit, Word Clouds are not the final destination; rather they are a dynamic stepping stone towards unravelling the story embedded within Qualitative data. Reach us at flowres.io, to see how we are helping brands shape storytelling; using GenAI and automated transcription.