Create visualizations.md

Author: zmuhls

visualizations.md

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+---
+title: Text Visualizations
+layout: default
+nav_order: none
+has_children: false
+---
+
+# Text Data Visualizations
+
+## Instructions for Students
+
+1. **Explore Voyant-Tools**  
+   Access [Voyant-Tools](https://voyant-tools.org/) and upload a text file or input a URL of a text source. You may choose a public domain text, your own writing, or another dataset relevant to the course.
+
+2. **Generate Visualizations**  
+   Experiment with different visualizations (e.g., Word Clouds, Trends, TermsBerry, etc.). Identify one visualization that you find insightful or compelling.
+
+3. **Embed Your Visualization**  
+   Use the "Share" or "Export" feature in Voyant-Tools to generate an embed code for your chosen visualization. Copy this embed code and include it in the markdown block provided below. 
+
+4. **Write Your Analysis**  
+   Add a brief analysis to accompany your visualization, addressing the following questions:
+   - **Tool Explanation**: Which Voyant-Tools feature did you use to generate your visualization, and how does it work?
+   - **Text Selection**: What text or dataset did you analyze, and why did you choose it?
+   - **Findings**: What patterns, trends, or insights did you discover through this visualization? What do these findings tell us about the text?
+
+## Visualization
+
+<div style="width: 800px; height: 600px; border: 1px solid #ddd;">
+   
+    <!-- Paste the embed code generated by Voyant-Tools below -->
+   
+</div>
+
+## Reflection and Analysis
+
+*Zach Muhlbauer, Poetry Foundation**
+I used the **Word Cloud** feature in Voyant-Tools. This tool visualizes the most frequent words in the text by displaying them in varying font sizes, proportional to their frequency. The data I selected was the Kaggle Poetry Database, a public dataset of Poetry Foundation poems. The visualization revealed that the most frequent nouns in the text are "time," "life," and "world," suggesting an emphasis on broader philosophical themes. The word "like" appears to top the charts, though, despite being a typical stopword filtered out of programmatic approaches to text data.