This example shows the interactions of a group of forum users in the form of a matrix. The interaction is computed by taking into account the number of posts the users wrote into threads with common tags (tags are chosen from a list of relevant keywords). To make clusters more evident we sort the users with a hierarchical clustering algorithm.
A linear luminance multihue color scale is used to improve the readability of the color encoding.
Treemap word cloud (similar to this example) showing the most used words in a forum about drugs. Thanks to the stemming process, the problem of counting singular and plural forms of the same word as different words is avoided, but the side effect is that some stems are no longer readable words with a meaning. Important keywords are emphasized (those not affected by the stemming).
This is a visualization for the European project CASSANDRA.
Treemap word cloud (similar to this example) showing the most popular tags used in a forum about drugs. Each thread of the forum may have one or more associated tags. The frequency is calculated keeping into account the number of posts in each thread. Important keywords are emphasized.
This is a visualization for the European project CASSANDRA.
Treemap word cloud (adapted from this example and keeping into account the bugfix used in this example). It shows the volume of messages with those keywords retrieved from Twitter. The technique used to color the words is taken from this example.
This is a visualization for the European project CASSANDRA.
An example of arc diagram visualizing repetitions of sequences of two or more characters in an italian tongue-twister.
Arc diagrams were first introduced in Wattenberg 2002. To avoid cluttering, not all repetions of sequences are shown; only the ones that are considered fundamental for the understanding of the structure are displayed. Refer to the paper to have more details. In this example, meaningful matches are manually selected.
| var compromised = true; |
| square = (x) -> x * x | |
| console.log square 5 | |
This experiment is like the previous one, but focused on verbs rather than nouns. The following picture shows the "islands" of the core verb taxonomy graph (click here to see the one for nouns):
A cycle between synsets is removed (because identified as a human error in Richens 2008), then the graph is fed into a longest-path untangler to produce a tree.
This experiment is similar to the previous one, but we attached word senses to the synsets, selecting only core noun senses (less than 5000).
The following image depicts the tree obtained for this graph after a longest path untangling (synsets are shown in red, while senses in blue):
