Data Visualization Tools for Text

Data visualization can be used to understand complex data sets, uncover insights, and present data to tell a captivating story.

In this article, we will talk about different data visualization tools for text data and demonstrate how InfraNodus fits into the existing DataViz ecosystem. But first, let's explore the different data visualization types as the tool that you choose will depend on the type of visualization you want to do. If you already know your requirements, feel free to skip to the next section where we examine different data visualization tools.


 

Data Visualization Types

Choosing the right tool depends on the type of data visualization we want to present. There are different data visualization types for text ranging from tag clouds and line charts to complex dashboards that help analyze data and find logical relationships within qualitative and numerical data.


1. Tag Clouds

Tag clouds (or word clouds) are the most typical visual text representations. They show the most frequent words: the more frequent the word, the bigger it is. Tag clouds are suitable for an overview, but they lack context: you can't see how they actually relate to one another, so their functionality is very limited. However, you can also generate a word cloud that takes a context into account automatically from text.


2. Bar Charts and Line Charts

Bar charts and line charts can be good for showing a trend, but using them with text data is limited because you need to decide which parameter they're going to represent. The most typical one would be the number of mentions of a term during a certain time period. For instance, how frequently the term "war" was mentioned during the year 2022.


3. Bubble Charts and Scatter Plots

This kind of text visualizations can be useful for clustering different text documents along a dual axis chart. You can show what types of texts exist in a certain corpus and how they relate to one another. For that, you'll need to choose the parameters that are represented by the X-axis and the Y-axis.


4. Mind Maps and Concept Maps

Mind maps can be useful for generating connections between different concepts. It's not so much a text visualization but rather a visualization of a certain system. The problem with most mind maps is that you need to use an hierarchical structure to think of your ideas, which is not always most suitable. However, there is a way to create mind maps from text without the hierarchies.


5. Network Graphs

Network diagrams are very powerful in representing text data. They can show the relations between the different terms as well as the clusters of topics that exist in a certain document or in a corpus of documents. This is the kind of visualization that requires the software to convert a text into a network and not so many tools can do that. Using InfraNodus text network visualization tool, you can represent your text as a network and use network science to get necessary insights from your data.


 

Data Visualization Tools Review

We can categorize the existing data visualization tools into the following three categories.

  1. Dashboard tools (such as Tableau or Power BI — used to make interactive dashboards connected to your internal sources of data)

  2. Open-source tools: in Python and Javascript (high flexibility, programming knowledge required, less visual appeal)

  3. Data visualization apps (e.g. InfraNodus)


1. Data Visualization Apps

If you don't need a complex dashboard and don't want to program yourself, your best bet is to choose a data visualization app. Your choice will depend on the data visualization type you need, but in general, we can recommend the following tools:

  1. InfraNodus — for text visualization: showing the main topics, the connections between them, clusters of documents. Can also visualize text clouds with a context. Free trial, starts at €9 per month.

  2. Voyant Tools — for visualizing text content and text statistics. Free.

  3. YWorks — a more complex and expensive data visualization platform.

  4. NetBase Quid and Primer AI — sophisticated text analysis suites, start from $50K a year.


2. Tableau and Power BI: Create Your Own Dashboards

If you need to create an interactive dashboard that can be updated in real-time, then you can use Tableau or Power BI.

Tableau is a leading data visualization tool that allows users to easily create interactive dashboards and charts. Tableau offers a free online version, Tableau Public, which allows users to share their visualizations with others. The good thing about Tableau is that you can connect it to any existing database and update your data live. It takes some time to learn, but if you're building a dashboard for continuous reference, it could be a good tool to use.

Power BI is a data visualization tool developed by Microsoft. It allows users to create interactive dashboards and reports using data from a variety of sources. Power BI can be downloaded and used for free, but there is also a paid version with more advanced features. Power BI may be a suitable option for those who are already using with Microsoft products for their database maintenance and want to create something for internal use. Tableau is prettier, Power BI might have more options for business users and better integration with your existing tools.


3. Open -ource Data Visualization Tools: Matplotlib, Plotly in Python, Chart.Js in Javascript

Open-source Python tools are also popular for data visualization. Python is a programming language that is widely used in data science and machine learning. There are many open-source Python libraries for data visualization, such as Matplotlib, Seaborn (statistical and mathematical visualizations), and Plotly (for advanced data science applications). These libraries offer a wide range of visualization options, from basic bar charts and line graphs to more complex visualizations like heat maps and 3D plots. They also offer some tools for visualizing text data, although they are more useful for quantitative, not qualitative data. It takes some time to learn how to use Python, but you can take ready-made code and try it out in Google Collab, and then deploy it in your own organization.

You can also use Javascript if you want something that will face the front user. Our favorite Javascript visualization libraries are Chart.Js (good for charts) and D3 (a framework for visualization).



 
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In conclusion, data visualization is a powerful tool for understanding complex data sets and uncovering insights that may be hidden within the text data. Unfortunately, as you can see, there is a gap in the market in that you can either get a complex dashboard-like system or a very simple tag cloud visualizer — and nothing in between, unless you are ready to program. We attempted to fulfill this gap with InfraNodus, which can visualize the main topics, most important keywords, topic evolution in time, as well as sentiment using a combination of network graphs and bar charts.



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