Jason Priem recently pinged me with a link to a project he’s working on: FeedViz. FeedViz provides several dimensions along which to explore and consume feeds: time (via a time series), tags (via a linearized tag cloud) and specific blogs (via a list). Selecting on any of these dimensions updates the display of the other 2. Finally, you can read posts that exist and the intersection of (the settings for) these dimensions.
The tag cloud is generated using “two numbers for each word:
- The first is frequency. Frequency says how many times a word is used per 1000 words. If you hover over a word, you'll see its frequency to the left of the frequency change value.
- The second is frequency change. Often, a word will be more (or less) popular than usual in a certain time period (for instance, "election" in early November). Frequency change measures that difference as a percentage: greener words are unusually popular; redder words are the opposite.”
While I really like the design, animations and implementation, I’m not convinced that the above approach is the best way to surface keywords. Of course, it depends on what the purpose of the keywords is (descriptive, discriminative, or trendive), but I’d love to see this stuff running on something like BLRT or TF.IDF.