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## People behind MySemCloud

#### Anaïs Villedieu

Anaïs Villedieu is a PhD student in the Algorithms & Complexity Group at TU Wien. She is interested in algorithm engineering, graph drawing and information visualization.

#### Martin Nöllenburg

Martin Nöllenburg is a full professor in the Algorithms & Complexity Group at TU Wien. His research interest is in graph drawing and network visualization, computational geometry, algorithm engineering and combinatorial optimization.

#### Michael Huber

Michael Huber is a graduate student in the Algorithms & Complexity Group at TU Wien.

## Frequently Asked Questions

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A word cloud is a representation of the most important words in a document, scaled to their importance, in a compact display. In a semantic word cloud, the distance between each words also matters, words are close if they are related, and should be far appart otherwise.

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We scan the input text to find which words are repeated most often to find the list of most important words. Then, we find out how often two words appear in the same sentence. If the appear together a lot, they are related. Finally, we place them in the plane using a system of forces that groups the words together. The forces are also stronger if the words are related to ensure they stick together.

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The main interactive component is the drag-and-drop function. You can move words around to different position, and the tool ensures that words are not stacked when you are done. Additionally, there are two modes you can enable to enhance the drag-and-drop:

- Fill holes: this will make keep the word cloud tightly packed after each move. You can also trigger it with just a button.
- Neighbors follow: if this is active and you move a word, a few words that are related to your word will follow it after you are done moving it.

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They describe the quality of the word cloud. There are three ways to describe it:

- Realized adjacencies: tells you how often words that are related touch in the word cloud. The value is higher if words that have a strong relationship are touching, instead of words that have a weak relationship.
- Distortion: tells you if the distance between words is similar to the strength of the relationship between words.
- Compactness: indicates if your words form a compact rectangle or if there is a lot of whitespace.

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The metric guides give you information about the quality of your layout and how to improve it with regards to each of the three metrics used.

- Realized adjacencies: when active, the important links between words are displayed. Green links occur between two words that are related and in contact, yellow edges represent two words that are related and not in contact. The links are wider if the relationship between two words is stronger. Consider breaking small green edges to repair large yellow ones. You can also right click a word to see all of its edges, as by default, only the largest yellow edges are displayed.
- Distortion: when active, it highlights words that are placed far from their neighbors and close to unrelated words. Distortion is not about ensuring that two words are in contact, only that their are close enough. If you right click a word you will see a heatmap. The dark green areas of the heatmap correspond to a better position for the word cloud have selected.
- Compactness: when active, it shows you the overall area taken up by the word cloud. If you want to make it smaller, move the red words towards gaps in the word cloud.

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MySemCloud has several means of saving your work. For starters you can undo any number of word movements to recover a previous word cloud. Additionally, you can at any point save a word cloud, to load it back later. You can save any number of word clouds. Also, the tool automatically save good layouts. If any if the values in the top reach a maximum, the word cloud is automatically saved.