How to Use Text Analytics in Power BI

While dashboards are great at showing numbers like revenue and clicks, they often miss a huge piece of the puzzle: words. Your business is swimming in textual data from customer reviews, support tickets, survey responses, and social media comments. This article will show you how to analyze that text directly within Power BI, turning opinions and feedback into clear, actionable insights.

What is Text Analytics and Why Should You Care?

Text analytics is the process of getting high-quality, structured information out of unstructured text. Instead of manually reading through thousands of comments one by one, you can use automated tools to quickly understand the main ideas, feelings, and topics being discussed.

Imagine you run an e-commerce store. You could use text analytics to:

• Analyze customer reviews: Instantly discover if the sentiment around a new product is positive or negative and find out why. Are people complaining about "slow shipping" or raving about "great quality"?
• Process survey feedback: Instead of just seeing star ratings, you can analyze open-ended comments to find out why people gave you that score.
• Monitor social media mentions: Keep a pulse on brand perception by automatically categorizing customer tweets or posts as positive, negative, or neutral.
• Find trends in support tickets: Identify recurring issues or feature requests that your customers are writing about, helping your product team prioritize what to build next.

This isn't about replacing human intuition, it's about amplifying it. It lets you listen to all of your customers at scale, not just the loud ones.

Getting Started: AI Insights in Power BI

Power BI has powerful, built-in AI features that make basic text analytics surprisingly easy. These tools, part of Azure Cognitive Services, are available directly within the Power Query Editor for users with Power BI Premium or Premium Per User (PPU) licenses.

Let's walk through the three main functions you can use.

Step 1: Load Your Text Data

First, you need to bring your data into Power BI. This could be a CSV or Excel file of customer reviews, survey export, or a database table of support tickets. For this example, let's assume you have a simple Excel file with two columns: Review ID and Review Text.

1. Open Power BI Desktop.
2. Go to the Home ribbon and click Get Data > Excel Workbook.
3. Locate and open your file.
4. Choose the correct sheet from the Navigator window and click Transform Data. This will open the Power Query Editor.

Step 2: Access the Text Analytics Tools

Within the Power Query Editor, you'll find the AI tools nested under the Text Analytics button. Make sure you select the column containing the text you want to analyze (in our case, Review Text), then click the button.

You may need to sign in to your organizational account with Power BI Premium privileges. Once authenticated, you will see three incredible options.

1. Sentiment Analysis (Detect Score)

This is the most common text analytics task. It reads a piece of text and assigns it a sentiment score from 0 (very negative) to 1 (very positive). A score around 0.5 is neutral or mixed.

• With your text column selected, click Text Analytics and choose Score sentiment.
• Power BI will communicate with the AI service and, after a moment, add a new column to your table called Score sentiment.

You’ll now have a numerical score for every review, making it easy to calculate an average sentiment or count the number of positive vs. negative reviews.

2. Key Phrase Extraction

This tool helps you quickly understand what people are talking about. It scans the text and pulls out the main talking points or topics.

• Select your Review Text column again.
• Click Text Analytics and choose Extract key phrases.
• A new column (Extract key phrases) will appear. This column will contain the most important terms from each review, often separated by commas (e.g., "fast delivery," "friendly customer service," "great product").

3. Language Detection

If you operate in multiple regions, this feature is for you. It automatically detects the language of the text and provides both the full language name (e.g., "English") and its ISO code ("en").

• Again, select your Review Text column.
• Click Text Analytics and choose Detect language.
• This adds two new columns: Detect language.name and Detect language.isoCode.

Once you've run these processes, hit Close & Apply in the Power Query Editor to load your newly enriched data into your Power BI data model.

How to Visualize Your Text Analysis Insights

Now that you have structured data instead of just raw text, you can visualize it! The power of text analytics comes alive when you can see the results on a BI dashboard.

Visualizing Sentiment

You can represent sentiment in several ways:

• Average Sentiment Score Card: Use a Card visual to display the simple average of your sentiment score column. This gives you a great top-line KPI for overall customer satisfaction.
• Sentiment Breakdown Column Chart: To see the number of happy vs. unhappy customers, you first need to categorize scores. Create a new column using a simple DAX formula:

Sentiment Category = IF(Reviews[Sentiment Score] > 0.65, "Positive", IF(Reviews[Sentiment Score] < 0.35, "Negative", "Neutral"))

You can then use a Bar Chart or Pie Chart to show the count of reviews for each Sentiment Category.

Visualizing Key Phrases with a Word Cloud

Word clouds are a classic and powerful way to visualize the key phrases you extracted. Bigger words represent more frequently mentioned topics.

Power BI doesn’t have a native word cloud, but you can easily add one as a custom visual from AppSource.

1. In the Visualizations pane, click the three dots (…) and select Get more visuals.
2. Search for "Word Cloud" in the AppSource marketplace – there are several great free options. Click Add to put it in your visualization pane.
3. Click the new Word Cloud icon to add it to your report canvas.
4. Drag your key phrases column into the "Category" field of the visual. Power BI will handle the rest, showing you the most talked-about topics at a glance.

Pro Tip: You can use your 'Sentiment Category' as a filter or legend on your dashboard. When you click on "Negative," your word cloud can update to show you a cloud of only the key phrases mentioned in negative reviews. This is where you find the real insights!

A Quick Practical Example: Analyzing Survey Responses

Let's tie it all together. You just received 500 responses from a customer survey that asked, "What is one thing we could do to improve our service?" You load the response data into Power BI.

1. You use the AI Insights > Score sentiment feature to find the feeling behind each comment.
2. You use AI Insights > Extract key phrases to pull out the main topics automatically.
3. You build a simple dashboard:

By filtering your dashboard to only negative comments, the word cloud reveals the biggest problems. Now, instead of a vague feeling that some customers are unhappy, you have a concrete, data-driven mandate: "We need to focus on reducing wait times and addressing feedback about high shipping costs."

Going a Step Further with DAX and Text Functions

The built-in AI tools are fantastic, but you can also perform simpler text analysis manually using Data Analysis Expressions (DAX). This is great when the built-in AI is not available or when you want more control.

A common use case is tagging records that contain a specific keyword. For instance, you could create a new column to identify any review that mentions "price."

In the Data view of Power BI, click New column and enter a DAX formula like this:

Mentions Price = CONTAINSSTRING(Reviews[Review Text], "price")

This will create a new column that returns TRUE if the review text contains the word "price" and FALSE if it doesn't. You can then create visuals based on this tag, such as comparing the average satisfaction score for customers who mention price versus those who don't.

Combine this technique with other DAX functions like LEN() (to measure comment length) or SEARCH() to continue slicing and dicing your textual feedback in new and powerful ways.

Final Thoughts

Analyzing text in Power BI turns qualitative feedback into quantitative, measurable data. By using AI Insights to understand sentiment and identify key themes, you can listen to all your customers at scale and build dashboards that reveal the story inside their words.

While Power BI's capabilities are incredibly powerful, there is a learning curve, and the manual process of setting up reports and learning DAX can be time-consuming, especially for busy teams. At Graphed, we created a tool that skips that entire learning curve. We connect directly to your marketing and sales data sources, allowing you to ask questions like "Show me a pie chart of customer sentiment based on our Shopify reviews this month" in plain English and instantly get a live, interactive chart for your dashboard.