word cloud in research paper

Create Engaging Word Cloud Visualizations from Your Research

• data visualization
• natural language processing

Many outstanding researchers and labs have created visualizations of their research using word clouds. In this post, I present a simple, automated “paper2wordcloud” workflow to create eye-catching word cloud visualizations. It combines the efficiency of automation with the power of human intuition and aesthetic sense. The figure below was created using my published papers .

The general steps in the workflow are:

• Collect PDF files representing your research (10 min).
• Run a Python script to extract the top words from the PDF files (10 min).
• Review, edit, and finalize the list of top words (20 min).
• Use a word cloud generator, adjust the look, and generate SVG (15 min).
• Convert the SVG file to a PDF/PNG file (5 min).

Now let’s dive into it.

Step 1: Collect your research

Collect all the PDF files that can represent your research, for example, papers, slides, posters, and proposals. Place all PDF files in a single, flat directory, without subfolders. The PDF files should be machine-readable, that is, the pages should not be scanned photocopies, and the text should be selectable in PDF viewers.

Step 2: Extract top words

2.1 install python.

Install Python if you haven’t. For macOS users, install Python via Homebrew :

This will install the latest maintained release of Python 3 provided by Homebrew.

2.2 Get text processing script and install dependency

Clone this GitHub repo: nanxstats/pdf-word-extraction . It contains a Python script I wrote for extracting meaningful words, as defined by a statistical model, from the PDF files.

Follow the workflow section in the repo readme to create a virtual environment in the cloned repository, activate it, and install the required Python packages into the virtual environment. This includes pypdf for PDF parsing, ftfy for text cleaning, and spaCy for natural language processing.

Everything below assumes you are in the directory with the virtual environment activated.

2.3 Run the script

Now, copy all the PDF files prepared in step 1 into pdf/ .

Then, run the Python script:

This will print the top 250 words and their frequencies.

Step 3: Review, edit, and finalize top words

Review the output and identify any words that should be removed or replaced. The common reasons include:

Removal : Words that are meaningful in general but not meaningful in your research context should be removed. Examples include “journal”, “conference”, “Figure”, “Table”, and author names.

Replacement : Uncommon proper nouns that should be stylized in a specific way can be fixed via replacement. The frequency counts for plural and singular forms of the same word can be merged via replacement, too.

To add word removal or replacement rules, open pdf_word_extraction.py . Edit the entries in the list words_to_remove and the key-value pairs in the dictionary replacements . Save and run the Python script again with the same command as before:

Check the output again. Since some words in the original output have now been removed or replaced, the words newly popped into the list might give you more words to remove or replace. Continue this review-edit-run cycle until the top 250 words looks perfect. For me, I ended up removing 50 words and establishing 12 replacement rules.

Each time after running the script, a top_words.txt will be generated or overwritten under the directory. We will use this file in the next step.

Step 4: Use the word cloud generator

Open top_words.txt , select all content, copy and paste into the word cloud generator described in my previous blog post , then click the “Refresh Word Cloud” button to generate an initial layout.

Adjust the graphical parameters based on your aesthetic preferences. Key parameters to consider include the color palette, font, scale transformation method, and the number of words to display.

Keep clicking the “Refresh Word Cloud” button until you achieve a layout you are satisfied with. Personally, I prefer a layout where all the major words are displayed horizontally. Click the “Download SVG” button to save the word cloud as an SVG file.

Step 5: Convert word cloud to a PDF/PNG file

See the appendix section of my previous blog post for a robust command-line workflow to convert the SVG file into a vector PDF file or a 300 DPI PNG file.

With these steps, you now have a professional word cloud visualization based on your research. Enjoy exploring your data in this visually engaging format!

This “paper2wordcloud” workflow demonstrates how to use Python to automate a seemingly difficult task that involves processing natural language data, while allows incorporating human knowledge and preferences. I’m quite amazed by how the text data processing toolchain in Python has advanced, making it a perfect choice for tasks like this.

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Word Clouds

When to Use:  At the end of a project

Estimated Time:  5 minutes for participants

Participants:  Young Children, Youth, Adults, Educators

Supplies: 

• Method of collecting responses by on-line polling, spreadsheet, or from a text document
• word it out
• polleverywhere
• You may have to edit participant responses to correct misspellings, capitalization, past and present tenses before generating your word cloud. 
• Different word cloud generators offer options for customizing color schemes, fonts, word orientation, etc.

Sample Topics: Ask participants to supply 3-5 descriptive words.

• Feelings and Reactions 
• Themes - subjects and topics of interest
• Values - what is important
• Favorites/ Least Favorites
• Creating live word clouds with Google Slides from Poll Everywhere
• How to interpret word cloud data from Boost Labs
• Tips on using Wordclouds.com  from Europlanet Society

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Enhancing Academic Work with Word Clouds

Word clouds are not just a beautiful visual representation of text; they are also an incredibly useful tool for academics. Whether you are a student, researcher, or educator, integrating word clouds into your daily academic life can bring numerous benefits. In this article, we will explore how you can leverage word clouds to enhance your academic work and how WordCloud.app can be the go-to tool for all your word cloud needs.

1. Visualizing Concepts and Ideas

Word clouds offer a unique way to visualize complex concepts and ideas. Instead of poring over pages of text, you can create a word cloud that highlights the most important keywords and phrases. This helps you to quickly grasp the main themes and relationships within your research or study material. With WordCloud.app, you can easily create custom word clouds by either entering your own text or analyzing web pages and books.

2. Analyzing Textual Data

As an academic, you often need to analyze large volumes of text, such as research papers, articles, or books. Word clouds can be a valuable tool for understanding the key themes and trends within your textual data. By creating a word cloud, you can identify recurring terms, visualize the frequency of specific words, and gain insights into the overall composition of the text. WordCloud.app’s ability to analyze web pages and books makes it a powerful tool for text analysis.

3. Presenting Findings and Data

When presenting your research or findings, word clouds offer an engaging and visually appealing way to present data. Instead of presenting a lengthy bullet-point list or overwhelming charts, you can use a word cloud to highlight the most important keywords and concepts. WordCloud.app provides a wide range of customization options, allowing you to choose different shapes, colors, and fonts to ensure your word cloud suits your presentation needs.

4. Collaborative Work and Brainstorming

Whether you are working on a group project or brainstorming ideas, word clouds can facilitate collaboration and creativity. By creating a collaborative word cloud, each team member can contribute their ideas, and the word cloud helps to visualize the collective input. WordCloud.app’s integrations with Figma and Miro make it seamless to create and share word clouds within your collaborative workspace.

5. Personalized Study Aids

Word clouds are not just for research and presentations; they can also serve as effective study aids. By creating a word cloud of key terms, definitions, or important concepts, you can have a visual reference to review and reinforce your understanding. Customizing the shape, colors, and fonts of the word cloud can make it visually appealing and enjoyable to study.

With its extensive features and user-friendly interface, WordCloud.app is the ideal tool for all your word cloud needs. From its curated collection of inspiring word clouds to its ability to analyze web pages and books, WordCloud.app offers a diverse range of possibilities to create stunning and meaningful visual representations of text.

So, whether you are a student looking to enhance your study materials, a researcher aiming to make your findings visually appealing, or an educator seeking creative teaching aids, WordCloud.app is the perfect companion to transform your academic work into captivating word clouds.

Visit WordCloud.app to unleash your creativity and discover the endless possibilities of word clouds today!

Related Posts

How word clouds can help language learners, how teachers and educators can use word clouds to enhance learning.

• Description

System Overview

In the last few years, word clouds have become a standard tool for abstracting, visualizing, and comparing text documents. For example, Word clouds were used in 2008 to contrast the speeches of then US presidential candidates Obama and McCain. A word cloud of a given document consists of the most important (or most frequent) words in that document. Each word is printed in a given font and scaled by a factor roughly proportional to its importance (the same is done with the names of towns and cities on geographic maps, for example). The printed words are arranged without overlap and tightly packed into some shape (usually a rectangle). Many practical tools, like Wordle, with its high quality design, graphics, style and functionality popularized word cloud visualizations as an appealing way to summarize the content of a webpage, a research paper, or a political speech. While similar tools are popular, most of them have a potential shortcoming: They do not visualize the relationships between the words in any way, as the placement of the words is completely independent of their context. But humans, as natural pattern-seekers, cannot help but perceive two words that are placed next to each other in a word cloud as being related in some way. In linguistics and in natural language processing if a pair of words often appears together in a sentence, then this is seen as evidence that this pair of words is linked semantically. When visualizing the given text with a word cloud, it makes sense to place such related pair of words close to each other. It helps to visually identify major topics in the input text.

Word clouds generated from titles of papers from FOCS, 1993-2013. left: The result produced by the Wordle tool: word placement, orientation, and colors are chosen arbitrarily; right: Semantics-preserving word cloud: semantically related words are drawn together and colored according to the automatically extracted clusters.

Word Cloud Generation

• Term Extraction: The input text is first split into sentences, which are then tokenized into a collection of words using Apache OpenNLP . Common stop-words such as "a", "the", "is" are removed from the collection. The remaining words are grouped by their stems using the Porter Stemming Algorithm, so that related words like "dance", "dancer", and "dancing" are reduced to their root, "danc". The most common variation of the word is used in the final word cloud.
• Ranking: In the next step we rank the words in order of relative importance. We use three different ranking functions, depending on word usage in the input text. Each ranking function orders words by their assigned weight (rank). Term Frequency is the most basic ranking function and one used in most traditional word cloud visualizations. Even after removing common stop-words, term frequency tends to rank highly many semantically meaningless words. Term Frequency-Inverse Document Frequency addressed this problem by normalizing the frequency of a word by its frequency in a larger text collection. The third ranking function is based on the LexRank algorithm. The algorithm is a graphbased method for computing relative importance of textual units using eigenvector centrality.
• Similarity Computation: Given the ranked list of words, we calculate a matrix of pairwise similarities so that related words receive high similarity values. We use three similarity functions depending on the input text: Cosine Similarity , Jaccard Similarity , and Lexical Similarity . In all cases for all pairs of words, the similarity function produces a value between 0, indicating that a pair of words is not related, and 1, indicating that words are very similar.

Algorithms and Implementation

• Semantic Word Cloud Representations: Hardness and Approximation Algorithms
• An Experimental Study of Algorithms for Semantics-Preserving Word Cloud Layout
• Improved Approximation Algorithms for Semantic Word Clouds