Introduction to Dashboarding for Text Analysis
Text analysis is all about finding the meaning behind your text data. But then what? In many ways, the insights you generate from analyzing text are only as good as the dashboards you use to visualize the data.
There exists a complex relationship between text analysis and dashboarding. Too much data makes a dashboard hard to interpret, while poor-quality text data (like what you find on social media) can mess with results.
Additionally, building a dashboard without carefully considering the end-user experience can spell disaster.
Here we discuss all things dashboard design for text analysis, identifying common errors, sharing best practices, and providing real-world examples.
Why Text Analysis Needs Dashboards
Text analytics offers powerful capabilities to transform raw textual data into structured, actionable insights. Organizations are increasingly leveraging NLP techniques such as sentiment analysis, entity extraction, and topic modeling to uncover trends hidden within customer reviews, social media, market reports, and more. The key to unlocking these insights is through interactive text analysis dashboards, which provide an intuitive interface for users to explore, visualize, and interpret complex datasets.
Displayr’s dashboarding capabilities allow users to create custom visualizations that integrate directly with text analytics outputs. For example, a market researcher could create a dashboard to monitor customer sentiment trends across different product categories or analyze social media content to identify emerging topics and key influencers.
Text analysis dashboards are crucial for many applications, including:
- Market Research: Uncovering customer sentiment and emerging trends through interactive dashboards
- Customer Experience: Analyzing feedback from surveys, reviews, and support tickets
- Competitive Intelligence: Tracking competitors’ mentions and sentiment across digital channels
- Brand Health Monitoring: Visualizing sentiment shifts in real-time
- Social Media Analytics: Analyzing Twitter, Facebook, and other social content for public opinion
These dashboards combine NLP techniques with rich, interactive visualizations to deliver insights in real time, making them invaluable tools for data-driven decision-making.
Key Benefits of Text Analysis Dashboards
The real-time, interactive nature of text analysis dashboards offers several key benefits for organizations looking to leverage text data for better decision-making:
- Track Sentiment and Emerging Trends: Visualize sentiment over time, identify shifts in public opinion, and track sentiment by topic, product, or market segment.
- Real-Time Decision Making: Stay on top of changing customer sentiment, competitor activities, and market dynamics with live data updates.
- Deeper Insights: Use NLP-powered visualizations like sentiment heatmaps, entity extraction charts, and topic clusters to reveal deeper insights that would be difficult to uncover with traditional analysis.
- Share Insights Across Teams: Share live dashboards with stakeholders across the organization for seamless collaboration and decision-making.
- Interactive Exploration: Allow users to drill down into specific data segments, filter results, and customize views to meet their needs.
Accurate text analysis and interactive dashboards in the same app.
Start a free trial of Displayr.
Best Practices for the Best Dashboards
So what do great dashboards have in common? Usually, they are easy to understand, have a clear purpose, and tell an interesting story. Some more best practices to keep in mind are:
- Define Clear Objectives: Know what you want to achieve with your dashboard—whether it's tracking customer sentiment, monitoring market trends, or comparing competitor performance.
- Data Quality: Clean and preprocess your data to ensure accurate analysis. Text normalization, deduplication, and error handling are essential steps to ensure clean input.
- User-Centric Design: Build dashboards that are intuitive for users. Incorporate interactive filters, drill-down options, and customizable views to let users explore the data that’s most relevant to them.
- Tailor Visualizations to Your Audience: Choose the right type of visualizations based on the goals of the analysis—sentiment heatmaps for customer feedback, network diagrams for entity extraction, and line charts for trend analysis.
- Regularly Update Your Data: Set up automated data pipelines to ensure that your dashboard always reflects the most current insights.
Common Mistakes
On the other hand, poor dashboards also usually share some similarities. Some pitfalls to look out for when building your own dashboard include:
- Clutter: More data does not always equal a better dashboard. In fact, overloading the dashboard with too much data and making it hard to read will lower the end-user experience.
- Messy data: One of the most important steps in any text analytics project is data cleaning. This extends into dashboarding, with unclean data translating to inaccurate results.
- Poor UX: Dashboards should always be intuitive; that is, the end-user should be able to successfully use it without explicit instructions. Think about how you can modify the layout of your dashboard so that it is self-explanatory.
- Ignoring data privacy: Be mindful of privacy regulations (e.g., GDPR) and ensure sensitive data is handled securely.
- Using old data: The underlying data of dashboards will vary on a case-by-case basis, but where possible, it is always best to use real-time data. This is what gives the end-user the ability to make decisions in real-time and is where the magic of dashboarding comes in.
Text Analysis Dashboard Example
So what does a text analysis dashboard that follows these best practices actually look like? Don't worry, we've got you.
This Customer Feedback Dashboard uses survey data about telecommunications providers to show how customer feedback shapes brand popularity. It is intuitive, interactive, and tells a clear story (and you can use it as a template for your next customer feedback survey).
Importantly, it seamlessly incorporates text data to provide greater context to the findings.
A great text analysis dashboard should be easy, interactive & intuitive.
Text Analysis and Dashboarding in the Same App?
Typically, building a great text analysis dashboard requires a host of different systems. You would need somewhere to process and clean your data, somewhere to analyze the text, and somewhere else to build the dashboard.
With Displayr, this can all be done in the same place. Easily upload your text data, analyze it with a few clicks, then choose from over 100 different visualizations to create the perfect dashboard.
