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What are the best ways to identify patterns in text data using data visualization?

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Text data is one of the most common and rich sources of information in data mining, but also one of the most challenging to analyze and visualize. Text data can contain various types of patterns, such as topics, sentiments, keywords, entities, relations, and more. How can you use data visualization to identify and explore these patterns effectively? In this article, you will learn some of the best ways to visualize text data using different techniques and tools.

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Kyle Vincent Rosario

Data and Insights Senior Associate Director @ IE Business School | Process Oriented Problem Solving with Data and…

1 Why visualize text data?

Visualizing text data can help you gain insights, communicate findings, and validate hypotheses from your data mining process. By using visual elements such as charts, graphs, maps, and networks, you can transform raw text into meaningful and interactive representations that can reveal patterns, trends, outliers, and relationships. Visualizing text data can also help you compare, contrast, and cluster different text sources, such as documents, tweets, reviews, or transcripts.

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Kyle Vincent Rosario

Data and Insights Senior Associate Director @ IE Business School | Process Oriented Problem Solving with Data and Analytics | Lifelong Tinkerer | Kyleidoscope
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Text data is some of the richest data sources we have but because of the difficulty of managing it, it is often ignored. By finding ways to visualize text data we are able to tap into this rich source of information and present it in an easily digestible way. Imagine all the information stored in the "Open comments" section in a survey. Of course most of the time they are left empty but those respondents who took the time to put a reply are usually very passionate about their opinion and can provide a lot of insight.

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2 What are the types of text data visualization?

Text data visualization can be classified into two broad categories: word-level and document-level. Word-level visualization focuses on the frequency, distribution, and association of individual words or phrases in a text corpus. Examples of word-level visualization techniques are word clouds, bar charts, histograms, and scatter plots. Document-level visualization focuses on the structure, similarity, and diversity of entire documents or text segments in a text corpus. Examples of document-level visualization techniques are dendrograms, heat maps, topic models, and network graphs.

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Kyle Vincent Rosario

Data and Insights Senior Associate Director @ IE Business School | Process Oriented Problem Solving with Data and Analytics | Lifelong Tinkerer | Kyleidoscope
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One thing we did when working on a text frequency project was to define a within document z-score and an across document z-score. This was to try and normalize 1. different document lengths, and then 2. compare the frequency of tokens across a bigger corpus of multiple documents. Then a simple bar chart with the thematic word groups we mapped was able to show us the "prototype" of each document and compare it to other documents.

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3 How to choose the right text data visualization technique?

When choosing a text data visualization technique, there are several factors to consider, such as the type, size, complexity of the data, the goal and audience of the analysis, and the available tools. Generally speaking, word-level visualization techniques can help explore the most frequent, relevant, or distinctive words or phrases in the data and identify patterns of co-occurrence or correlation. Document-level visualization techniques can be used to explore the overall structure, similarity, or diversity of the text data and detect patterns of grouping, clustering, or classification. Combining multiple visualization techniques can complement and cross-validate each other and provide different perspectives on the data. Finally, interactive visualization techniques can enable user feedback, exploration, and customization.

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4 How to prepare your text data for visualization?

Before visualizing your text data, you need to perform preprocessing steps to clean, normalize, and transform it into a suitable format. This includes removing unwanted characters, converting words to lowercase or using case-sensitive analysis, removing stop words that do not convey much meaning, applying stemming or lemmatization to reduce words to their root form, tokenizing text into smaller units, vectorizing it into numerical representations, and reducing the number of features or dimensions in the data. All these steps are necessary for successful text data visualization.

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Kyle Vincent Rosario

Data and Insights Senior Associate Director @ IE Business School | Process Oriented Problem Solving with Data and Analytics | Lifelong Tinkerer | Kyleidoscope
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Aside from the steps mentioned here, in a project we were working on, we took the time to do a preprocessing of the corpus to get a list of all the words (less stop words, stemmed, etc) and then map these words to thematic word groups. For our use case, this could have been mapping the word "start-up" or "innovate" to the thematic word group "Entrepreneurship & Innovation".

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5 What are the tools and libraries for text data visualization?

There are many tools and libraries that can help you visualize your text data, depending on your preference, skill level, and platform. Tableau is a powerful and user-friendly data visualization software that can create word clouds, bar charts, histograms, scatter plots, and heat maps. Power BI is another business intelligence and data visualization software with similar capabilities. Python is a general-purpose programming language with libraries and packages for data analysis and visualization. R is a statistical programming language with packages for data analysis and visualization. Both Python and R can create word clouds, bar charts, histograms, scatter plots, dendrograms, topic models, network graphs, and more.

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Kyle Vincent Rosario

Data and Insights Senior Associate Director @ IE Business School | Process Oriented Problem Solving with Data and Analytics | Lifelong Tinkerer | Kyleidoscope
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In my experience, Python is the way to go and with the all the available tutorials online, it makes text wrangling and visualization easy.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best ways to identify patterns in text data using data visualization?

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1 Why visualize text data?

2 What are the types of text data visualization?

3 How to choose the right text data visualization technique?

4 How to prepare your text data for visualization?

5 What are the tools and libraries for text data visualization?

6 Here’s what else to consider

Data Mining

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What are the best ways to identify patterns in text data using data visualization?

1

2

3

4

5

6

1 Why visualize text data?

2 What are the types of text data visualization?

3 How to choose the right text data visualization technique?

4 How to prepare your text data for visualization?

5 What are the tools and libraries for text data visualization?

6 Here’s what else to consider

Data Mining

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