Turn any block of text (survey answers, speech transcripts, support tickets) into a single image where the words people used most are the words you see first. One template, tuned for how you clean the text before it renders.
Un coup d'œil rapide à ce qui se trouve ici : cliquez pour ouvrir l'un d'entre eux.
Word cloud templates solve one problem: showing which words dominate a body of text without making anyone read the whole thing. Feed in a transcript, a batch of open-ended survey responses, or a list of tags, and the chart sizes each word by how often it shows up. It's the fastest way to get a read on a text dataset before you decide whether it's worth analyzing more rigorously.
There's only one chart type in this category, but the result you get depends entirely on what happens before the words hit the canvas. Stop-word handling matters most: strip out "the", "and", "a" and similar filler, or they'll dominate every cloud regardless of what the text is actually about. Stemming or lemmatizing helps too: without it, "customer", "customers" and "customer's" get counted as three separate words and split a signal that should be one bar's worth of weight. PlotSet's word cloud template handles common English stop-words automatically and lets you extend the exclude list with your own terms, useful when a client name or product name shows up in every row and drowns out everything else.
Be honest about what a word cloud is for. It's an opening visual, not an analytical one: great for a slide that needs to say "here's roughly what people talked about" in half a second, bad for anything where a reader needs to compare two counts precisely. Big and small are obvious at a glance; medium-versus-medium is not, because word size scales with frequency, not with a fixed, readable axis the way a bar's length does.
Chacun ouvre son propre créateur gratuit avec les données d'exemple déjà chargées.
You've got 500 open-text survey responses and thirty seconds on a slide to say what they were about. A word cloud gives the room a gut sense of the dominant themes before you dig into any of them individually.
Scanning support tickets or reviews for one word that jumps out oddly large (a bug name, a feature nobody expected people to mention) is exactly what a cloud is good at, since it isn't trying to rank anything exactly in the first place.
Blog tags, product keywords, hashtag sets: anywhere the underlying data is already discrete terms rather than free-flowing prose, so there's little cleanup needed before the frequencies mean something.
Du tableur au graphique publié, étape par étape.
It comes with sample text bound in, so you can see the expected input shape (either raw text or a two-column word/count list) before you bring your own.
Drop in raw text, a CSV of survey responses, or a pre-counted word/frequency table if you've already done the counting elsewhere.
Remove stop-words (on by default), add your own excluded terms, like company names or filler words specific to your dataset, and decide whether to merge case variants.
Publish for an interactive embed, or export a static PNG or SVG for a deck, report or social post.
Ouvrez l'éditeur et partez d'un modèle quelconque de cette catégorie : les données d'exemple sont déjà chargées.
Ouvrir l'éditeurWord size scales with how many times that word appears in the source text, after stop-words are removed. The most frequent term renders largest, and size drops off from there. It's a visual ranking, not a chart with a labeled, measurable axis.
Gratuit pour commencer avec plus de 300 modèles. Aucune carte de crédit requise.
