Word Clouds as Common Voices: LLM -Assisted Visualization of Participant-Weighted Themes in Qualitative Interviews (2025)

Abstract: Word clouds are a common way to summarize qualitative interviews, yet traditional frequency-based methods often fail in conversational contexts: they surface filler words, ignore paraphrase, and fragment semantically related ideas. This limits their usefulness in early-stage analysis, when researchers need fast, interpretable overviews of what participant actually said. We introduce ThemeClouds, an open-source visualization tool that uses large language models (LLMs) to generate thematic, participant-weighted word clouds from dialogue transcripts. The system prompts an LLM to identify concept-level themes across a corpus and then counts how many unique participants mention each topic, yielding a visualization grounded in breadth of mention rather than raw term frequency. Researchers can customize prompts and visualization parameters, providing transparency and control. Using interviews from a user study comparing five recording-device configurations (31 participants; 155 transcripts, Whisper ASR), our approach surfaces more actionable device concerns than frequency clouds and topic-modeling baselines (e.g., LDA, BERTopic). We discuss design trade-offs for integrating LLM assistance into qualitative workflows, implications for interpretability and researcher agency, and opportunities for interactive analyses such as per-condition contrasts (“diff clouds”).

## introduction qualitative interviews are a cornerstone of hci practice: they capture lived experience, tacit knowledge, and situated rationales that are difficult to elicit through logs or lab tasks alone @xcite . but precisely because conversational data are rich, early-stage sensemaking can be slow and brittle. time-constrained teams often rely on word clouds to orient themselves and to communicate initial patterns. word clouds help researchers surface recurring terms and communicate high-level themes to stakeholders @xcite . in principle, a quick visualization that "shows what people talked about" is invaluable. in practice, however, frequency-based word clouds tend to reflect how people talk rather than what they mean. this misalignment is acute for spoken transcripts. even with stop-word removal, the statistical surface of talk often dominates frequency ranks, such as disfluencies ("uh"), discourse markers ("like", "you know"), and coordination ("and"). moreover, participants rarely reuse identical strings when describing similar concerns. one person may say "it felt in the way," another "kind of distracting," another "i kept noticing the device," and a fourth "it made me self-conscious." traditional clouds fragment these into separate tokens, spreading salience thinly across synonyms and paraphrases. the resulting picture understates a theme's breadth and overstates lexical quirks, leaving analysts to manually reconcile meaning after the fact. in our motivating study, clinicians and participants evaluated different recording-device configurations intended for psychiatric assessment. when we generated standard frequency clouds per device, familiar problems reappeared: conversational scaffolding rose to the top; multi-word concerns broke into stems; semantically aligned reactions (e.g., "distracting," "in the way," "felt watched") were scattered. the clouds neither matched researcher notes nor helped communicate trade-offs to stakeholders. a different aggregation principle was needed. recent advancements in large language models (llms) present new opportunities for enhancing qualitative analysis @xcite . models such as llama 3.3 can process long passages of unstructured text, identify latent topics, and recognize semantically important terms even when they are phrased differently across transcripts @xcite . these capabilities make llms well-suited for tasks like summarization and topic extraction, which are core components of qualitative synthesis. in our use case, llms make a new design space feasible. rather than counting words, we can ask a model to reason about concepts, recognize paraphrases, and collapse near-synonyms-capabilities that have matured as models improved long-context understanding. but naively inserting llms can reduce transparency. our design goal, therefore, is to preserve the immediacy and communicability of word clouds while shifting the unit of analysis from tokens to concepts, and the weighting from raw counts to the breadth of mention across participants. in effect, we want a cloud that answers the question analysts and stakeholders actually ask: "how many people brought this up?" we contribute a method and artifact that operationalize this shift in a way that fits qualitative workflows. our open-source tool, themeclouds, leverages llama 3.3 to assist in generating semantic word clouds from qualitative interview transcripts. rather than relying solely on term frequency, the tool uses llm reasoning to extract salient terms and conceptually related groupings, producing visualizations that better reflect the themes embedded in natural dialogue. by incorporating lightweight user control, the system balances llm assistance with researcher agency, supporting interpretation while preserving transparency and flexibility. our work builds on prior literature in textual visualization and qualitative coding tools @xcite . while previous approaches have highlighted the risks of misleading word clouds or opaque model outputs, we aim to demonstrate how thoughtful design centered around customization and interpretability can help researchers co-construct word clouds with llms in qualitative workflows. the remainder of this paper describes the architecture and design decisions behind the system, demonstrates its application to interview data, and reflects on broader implications for llm-assisted tools in qualitative analysis. our contribution is methodological and pragmatic. we do not claim a new theory of qualitative analysis; instead, we provide a lightweight, defensible, and participant-weighted alternative to frequency clouds that better aligns early-stage summaries with how analysts reason and report. we show how to integrate llm assistance without obscuring the analytic process, emphasizing controls, artifacts, and audit trails that allow researchers to trust, contest, and adapt outputs. ## methods themeclouds is designed to assist researchers in generating word clouds from qualitative interview transcripts by using llms to surface salient, semantically meaningful concepts, rather than relying on surface-level word frequency. the pipeline consists of three key stages: (1) identifying candidate concepts across a corpus, (2) mapping those concepts to individual transcripts, and (3) aggregating the results to produce a word cloud visualization. our system prioritizes topic relevance, clarity, and interpretability over lexical frequency or length. figure 1 outlines the proposed workflow. we formalize the shift from tokens to concepts and from frequency to breadth. let @xmath0, . . . , t m } be transcripts (one per participant for a given condition) and let @xmath1, . . . , c n } be short concept-phrases proposed by an llm for the corpus. for each transcript t and concept c, the mapping step produces a binary assignment y(t, c) ∈ {0, 1} indicating whether the concept is clearly present in the transcript (the artifact optionally supports a soft score p(t, c) ∈ [0, 1] with threshold τ for binarization). the breadth of concept c is: the number of unique participants whose transcripts include the concept. the visual weight for c is w(c) = g(b(c)), where g(•) is a monotone scaling (linear by default; logarithmic and square-root options aid mid-rank legibility). we also support condition-wise contrasts by rendering ∆b(c) = b a (c)b b (c) to make differences across device configurations glanceable. ## human-centered design considerations a tool succeeds in hci not only by being accurate, but by fitting how people actually work. we therefore prioritized five properties. ## qualitative evaluation to assess the utility of our approach in a real-world setting, we applied it to a set of qualitative interviews conducted as part of a clinical psychology research study. 31 participants evaluated five webcam setups for psychiatric outpatient clinical assessments, producing 155 interviews with a clinical research coordinator. these conversations were conducted as one-hour in-person session, in a naturalistic dialogue format, as participants and the clinical research coordinator collaboratively evaluated different hardware configurations. audio was transcribed with whisper @xcite . for a representative device condition (31 transcripts), figure 2 compares a standard frequencybased word cloud with nltk stop-word removal @xcite with our llm-assisted word cloud (b). despite identical source data, the frequency cloud elevates general discourse terms and fragments multi-word concerns, while the llm cloud foregrounds concrete, device-specific ideas consistent with researcher notes. what the numbers mean. because our weights are counts of unique participants, the magnitude of a label directly translates to breadth. if "distracting" appears in 20 of 31 transcripts for a device, its visual prominence is immediately defensible-helpful for design reviews and irb or clinical discussions where conservative, populationgrounded claims are preferred. to situate the approach among common baselines, we also trained topic models such as lda and bertopic @xcite @xcite on the full 155-document corpus. as in while these observations are not a controlled user study, they illustrate a pattern we frequently saw during analysis: people-weighted concept clouds provide a more faithful "first glance" at what mattered to participants than token frequency or off-the-shelf topic models in this setting. it can effectively support researchers in identifying salient themes from conversational transcripts, even without structured codes or annotations. ## discussion and limitations our tool demonstrates how large language models can be leveraged to assist in synthesizing qualitative feedback through semantic word clouds, offering an accessible, low-overhead entry point into exploratory analysis. while initial use cases show alignment with human interpretation, there are important limitations to consider. ## artifact our open-source themeclouds package foot_0 includes: ## conclusion we introduced themeclouds, a participantweighted, concept-level approach to word clouds using llms to count who raised which ideas, aligning early-stage summaries with the way hci and ux analysts argue salience. in an audiovisual (av) study for clinical assessment, the method surfaced actionable concerns that frequency clouds and topic-modeling baselines obscured. by emphasizing transparency, agency, and auditability, it bridges nlp advances and qualitative practice, offering a pragmatic step toward interactive, humancentered, llm-assisted analysis.