A tag cloud is a visual representation for text data, typically used to depict keyword metadata (tags) on websites, to visualize free form text or to analyses speeches( e.g. election’s campaign). Tags are usually single words, and the importance of each tag is shown with font size or color. This format is useful for quickly perceiving the most prominent terms and for locating a term alphabetically to determine its relative prominence [1].
Depending on the level of sophistication, calculating positioning in a given space can become quite complex [2, 3]. If you need external dependencies, make sure to move this issue to the FsLab repository
Ideally, you start prototyping in a scripting or notebook environment where you can iterate fast. installation instructions can be found here: https://plotly.net/#Installation
Charts like this that are using baseline trace types to create a new chart type should only be implemented in the top-level Chart API. An example where this is already done is the Range chart that combines a set of differently styled line charts.
Ideally prevent text processing and just focus the plot on creating the tag cloud based on occurrence in a collection of strings. Preprocessing should be done in the pipeline before applying the visualization technique.
Hints (click to expand if you need additional pointers)
- you can position text on a scatterplot and hide the markers to only show text
- in a more sophisticated manner, you can also draw [shapes](https://plotly.net/reference/plotly-net-layoutobjects-shape.html) on a plot that are either boxes containing text or even svg paths representing the text.
Description
A tag cloud is a visual representation for text data, typically used to depict keyword metadata (tags) on websites, to visualize free form text or to analyses speeches( e.g. election’s campaign). Tags are usually single words, and the importance of each tag is shown with font size or color. This format is useful for quickly perceiving the most prominent terms and for locating a term alphabetically to determine its relative prominence [1].
Depending on the level of sophistication, calculating positioning in a given space can become quite complex [2, 3]. If you need external dependencies, make sure to move this issue to the FsLab repository
Example
References
Pointers
Hints (click to expand if you need additional pointers)
- you can position text on a scatterplot and hide the markers to only show text - in a more sophisticated manner, you can also draw [shapes](https://plotly.net/reference/plotly-net-layoutobjects-shape.html) on a plot that are either boxes containing text or even svg paths representing the text.