How to Do Text Analysis in Tableau

Buried in your customer reviews, support tickets, and open-ended survey responses is a goldmine of information. But how do you get it out? With a few clever techniques, you can turn Tableau into a powerful tool for text analysis, helping you transform blocks of raw text into clear, visual stories. This guide will walk you through how to prepare your data and use Tableau’s built-in functions to find meaningful patterns in your text.

What Exactly Is Text Analysis?

Text analysis, sometimes called text mining, is the process of extracting high-quality, actionable information from text. Instead of reading hundreds of individual reviews, you can use text analysis to quickly identify the most common words, themes, and sentiments. For a business, this can mean understanding:

• What features users praise most often in App Store reviews.
• Which recurring problems crop up in customer support emails.
• How sentiment on social media changes during a marketing campaign.

Bringing this analysis into Tableau allows you to combine these qualitative insights with your quantitative data. You can finally see how reviews mentioning "slow service" correlate with lower sales on certain days or how NPS scores change based on keywords in feedback.

First Things First: Preparing Your Text Data

The quality of your analysis depends entirely on the quality of your data. Before you pull your text into Tableau, running through a few simple data cleaning steps can save you a lot of headaches later on. Think of it like organizing your kitchen before you start cooking - it makes the whole process smoother.

You can do this using a dedicated tool like Tableau Prep, but for most simple cases, a spreadsheet like Excel or Google Sheets will work perfectly.

Essential Cleaning Steps:

• Make it Lowercase: Convert all your text to a single case (usually lowercase). This ensures that "Service," "service," and "SERVICE" are all treated as the same word, preventing them from being counted separately.
• Remove Punctuation: Periods, commas, and question marks don't usually add analytical value. Removing them helps isolate the words themselves.
• Trim Extra Spaces: Use a TRIM function to get rid of any leading or trailing spaces that could mess with your functions.

For more advanced analysis, you might also consider removing "stop words" — common words like "the," "is," "and," "a" that don’t carry much meaning. While you can do this during prep, it's also possible to filter them out inside Tableau.

Simple Text Analysis with Tableau's Built-in Functions

You don't need complex scripts to start finding insights. Tableau has several powerful string (text) functions that are perfect for foundational text analysis. Let's explore a few with a hypothetical dataset of coffee shop reviews.

Find Keywords with the CONTAINS() Function

The easiest way to start is by searching for specific keywords. The CONTAINS() function checks if a string of text includes a specific substring you define. It returns a simple TRUE or FALSE, which is perfect for categorizing your data.

Imagine you want to know how many reviews mention the word "coffee." You can create a calculated field to do this.

1. Go to Analysis > Create Calculated Field.
2. Name your field something descriptive, like "Mentions Coffee."
3. Enter the formula:

CONTAINS([Review Text], "coffee")

This formula looks inside your "Review Text" column and flags every row containing the word "coffee" as TRUE. Now you can drag this new 'Mentions Coffee' field to the Columns shelf and the COUNT of records to the Rows shelf to create a simple bar chart comparing reviews that mention coffee to those that don't.

You could create similar calculated fields for "service," "price," or "atmosphere" to quickly segment your feedback into key topics.

Building a Word Cloud

A word cloud is a classic text visualization that shows the most frequent words in a body of text. While fun to look at, creating one in Tableau requires a bit more data restructuring. To make a word cloud, you need your data formatted so that each word is on its own row.

This process is called tokenization. While challenging inside Tableau Desktop alone, it's straightforward using Tableau Prep or even the Text-to-Columns feature in Excel/Google Sheets followed by a pivot step.

Once your data is shaped with one word per row, creating the word cloud is simple:

1. Drag your "Words" dimension to Text on the Marks card.
2. Drag the Count of Words to Size on the Marks card.
3. Change the dropdown on the Marks card from Automatic to Text.

And there you have it! Tableau will display the words, sizing them by how frequently they appear. You can further refine it by adding a filter to exclude common stop words or adding COUNT(Words) to the Color mark to create a gradient effect.

Splitting Text with the SPLIT() Function

What if your data contains multiple keywords in a single column, separated by a comma (e.g., "coffee, friendly, morning")? The SPLIT() function is your best friend here. It can break up text based on a delimiter you define, like a comma or a space.

The formula looks like this:

SPLIT([Your Text Field], "delimiter", token number)

For example, if you have a field called [Review Tags] with values like "slow service, cold coffee, good music," you could extract each part:

• SPLIT([Review Tags], ",", 1) would return "slow service".
• SPLIT([Review Tags], ",", 2) would return "cold coffee".

You can create separate calculated fields for each part to analyze them individually. This is useful for parsing out recurring themes when your data source provides tagged information.

Taking it Further with Python and R Integration

For more sophisticated text mining like sentiment analysis (determining if text is positive, negative, or neutral) or topic modeling, you'll want to leverage the power of Python or R. Tableau can connect directly to these programming languages through tools called TabPy (for Python) and Rserve (for R).

How Does it Work?

At a high level, the process is simple:

1. You set up a connection between Tableau and an active Python or R server.
2. You write a calculated field in Tableau that sends your text data to a specific script.
3. The Python/R script runs its analysis (e.g., using a library like TextBlob to calculate a sentiment score).
4. The script sends the result — like a sentiment score from -1 to 1 — back to Tableau for each row of your data.

Once you have that sentiment score, you can visualize it just like any other measure. You could create a line chart showing average brand sentiment over time or a map showing sentiment by region. This integration does require some coding knowledge and setup, but it unlocks an entirely new dimension of text analysis that isn't possible with Tableau's built-in functions alone.

Final Thoughts

Analyzing text data in Tableau opens the door to understanding the "why" behind your numbers, whether you're using simple keyword searches or complex sentiment analysis models. By getting comfortable with functions like CONTAINS() and structuring your data for visualizations like word clouds, you can begin to extract real insights from customer feedback with tools you already have.

The biggest challenge is often just getting all your data in one place — from social media, your survey tools, and your CRM — before the analysis can even begin. At Graphed, we remove this friction by connecting directly to your marketing and sales platforms. Instead of manually wrangling data, you can ask questions in plain English, and we build live, interactive dashboards instantly, turning hours of tedious prep work into a 30-second conversation.